2.793

                    2018影響因子

                    (CJCR)

                    • 中文核心
                    • EI
                    • 中國科技核心
                    • Scopus
                    • CSCD
                    • 英國科學文摘

                    留言板

                    尊敬的讀者、作者、審稿人, 關于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

                    姓名
                    郵箱
                    手機號碼
                    標題
                    留言內容
                    驗證碼

                    人臉活體檢測綜述

                    蔣方玲 劉鵬程 周祥東

                    蔣方玲, 劉鵬程, 周祥東. 人臉活體檢測綜述. 自動化學報, 2021, 47(8): 1799-1821 doi: 10.16383/j.aas.c180829
                    引用本文: 蔣方玲, 劉鵬程, 周祥東. 人臉活體檢測綜述. 自動化學報, 2021, 47(8): 1799-1821 doi: 10.16383/j.aas.c180829
                    Jiang Fang-Ling, Liu Peng-Cheng, Zhou Xiang-Dong. A review on face anti-spoofing. Acta Automatica Sinica, 2021, 47(8): 1799-1821 doi: 10.16383/j.aas.c180829
                    Citation: Jiang Fang-Ling, Liu Peng-Cheng, Zhou Xiang-Dong. A review on face anti-spoofing. Acta Automatica Sinica, 2021, 47(8): 1799-1821 doi: 10.16383/j.aas.c180829

                    人臉活體檢測綜述

                    doi: 10.16383/j.aas.c180829
                    基金項目: 

                    國家重點研發計劃 2018YFC0808300

                    國家自然科學基金 61806185

                    國家自然科學基金 61802361

                    國家自然科學基金 61602433

                    詳細信息
                      作者簡介:

                      蔣方玲?? 中國科學院重慶綠色智能技術研究院博士研究生. 2012年獲得天津大學計算機科學與技術專業碩士學位. 主要研究方向為人臉活體檢測, 計算機視覺與模式識別.E-mail: jiangfangling@cigit.ac.cn

                      劉鵬程?? 中國科學院重慶綠色智能技術研究院助理研究員. 2016年獲得中國科學院自動化研究所工學博士學位. 主要研究方向為人臉識別, 跨領域圖像識別. E-mail: liupengcheng@cigit.ac.cn

                      通訊作者:

                      周祥東   中國科學院重慶綠色智能技術研究院副研究員. 2009年獲得中國科學院自動化研究所工學博士學位. 主要研究方向為文字識別, 文檔分析, 人臉識別. 本文通信作者.E-mail: zhouxiangdong@cigit.ac.cn

                    • 本文責任編委 劉青山

                    A Review on Face Anti-spoofing

                    Funds: 

                    National Key Research and Development Program of China 2018YFC0808300

                    National Natural Science Foundation of China 61806185

                    National Natural Science Foundation of China 61802361

                    National Natural Science Foundation of China 61602433

                    More Information
                      Author Bio:

                      JIANG Fang-Ling?? Ph. D. candidate at Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences. She received her master degree from Tianjin University in 2012. Her research interest covers face anti-spoofing, computer vision, and pattern recognition

                      LIU Peng-Cheng?? Research assistant at Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences. He received his Ph. D. degree from the Institute of Automation, Chinese Academy of Sciences in 2016. His research interest covers face recognition and cross-domain image recognition

                      Corresponding author: ZHOU Xiang-Dong   Associate professor at Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences. He received his Ph. D. degree from the Institute of Automation, Chinese Academy of Sciences in 2009. His research interest covers handwriting recognition, ink document analysis, and face recognition. Corresponding author of this paper
                    • Recommended by Associate Editor LIU Qing-Shan
                    • 摘要: 人臉活體檢測是為了提高人臉識別系統安全性而需要重點研究的問題.本文首先從人臉活體檢測的問題出發, 分個體、類內、類間三個層面對人臉活體檢測存在的困難與挑戰進行了闡述分析.接下來, 本文以算法使用的分類線索為主線, 分類別對人臉活體檢測算法及其優缺點進行了梳理和總結.之后, 本文就常用人臉活體檢測數據集的特點、數據量、數據多樣性等方面進行了對比分析, 對算法評估常用的性能評價指標進行了闡述, 總結分析了代表性人臉活體檢測方法在照片視頻類數據集CASIA-MFSD、Replay-Attack、Oulu-NPU、SiW以及面具類數據集3DMAD、SMAD、HKBU-MARsV2上的實驗性能.最后本文對人臉活體檢測未來可能的發展方向進行了思考和探討.
                      Recommended by Associate Editor LIU Qing-Shan
                      1)  本文責任編委 劉青山
                    • 圖  1  傳統人臉識別技術的安全性缺陷

                      Fig.  1  Vulnerability of conventional face recognition system

                      圖  2  不同類別假體人臉示例

                      Fig.  2  Examples of spoofing faces

                      圖  3  Replay-Attack數據集中的假體人臉

                      Fig.  3  Spoofing faces of Replay-Attack

                      圖  4  人臉活體檢測方法分類

                      Fig.  4  Classification of face anti-spoofing methods

                      圖  5  各類人臉活體檢測方法性能分布圖

                      Fig.  5  Performance comparison of different category of face anti-spoofing methods

                      表  1  主流人臉活體檢測方法總覽

                      Table  1  Brief overview of face anti-spoofing methods

                      一級類別 二級類別 分類特征 防范的假體人臉 算法優點 算法缺點
                      交互式人臉活體檢測 基于隨機動作的方法 用戶配合的動作: 點頭、抬頭、眨眼、閉眼、遮擋眼睛、揚眉、皺眉、笑臉、吐舌頭、張嘴[8, 12] 照片、視頻 對二維類假體人臉準確率高, 通用性強 需要用戶配合, 用戶體驗差, 不能防止眼部、嘴部挖洞的面具攻擊, 適用范圍窄
                      基于唇語聲音混合的方法 朗讀一個數字串、一段文字時的唇語與聲音[10-11]
                      非交互式人臉活體檢測 基于圖像紋理的方法 LBP、HOG、Gabor等描述符從灰度圖中抽取的灰度紋理特征[15-17, 21, 23, 30-33] 照片、視頻、面具 容易實現, 計算量少, 單張圖片可預測結果, 速度快 容易被拍攝設備、光照條件、圖像質量影響, 跨數據集通用能力不強
                      LBP、LPQ等描述符從HSV, YCbCr顏色空間圖像中抽取的顏色紋理特征[20, 35, 39]
                      基于圖像質量的方法 手工設計特征抽取圖像鏡面反射、顏色分布、清晰度方面的圖像質量特征[43-45] 照片、視頻 針對單類假體人臉的跨數據集通用能力相對強, 速度快 需要根據假體人臉的類別設計具體特征, 跨假體類型的通用能力不強, 需要高質量圖像, 難以抵御高清啞光照片視頻攻擊
                      基于生命信息的方法 光流法、運動成分分解檢測活體不自主地眨臉部、唇部的微運動[48, 51-52, 56] 照片 對照片類假體人臉準確率高, 通用性高 需要視頻為輸入
                      計算量大, 速度慢
                      難以防范視頻攻擊
                      對假體制造的微運動魯棒性不強
                      遠程光學體積描記術(rPPG)信息檢測待測 面具 特定約束條件下準確率高 需要視頻為輸入
                      魯棒性不強, 受外界光照、個體運動的影響大
                      基于其他硬件的方法 近紅外圖像特征[62-68] 照片、視頻、面具 準確率高 需要增加新的昂貴硬件
                      設備采集、處理圖像的時間增加
                      短波紅外圖像特征[69]
                      熱紅外圖像特征[70]
                      400 nm至1 000 nm的多個波段圖像特征[71-72]
                      光場圖像信息[73-74]
                      深度圖像信息[75-78]
                      基于深度特征的方法 從頭訓練CNN抽取深度特征分類[79-80, 83]、利用預訓練的ResNet-50、VGG等模型抽取特征[84?85, 87] 照片、視頻、面具 相對來說, 準確率較高 模型參數多, 計算量大, 訓練時間長
                      過擬合問題
                      對數據量和數據豐富性上有高要求
                      深度特征與手工特征融合[85, 95-97]
                      三維卷積抽取時空深度特征[93-94]
                      混合特征類方法 紋理信息和運動生命信息的混合[17-19, 25, 85, 93-94, 97, 99-101] 照片、視頻、面具 融合多特征的優點
                      提升識別準確率和通用性
                      計算量、存儲增大, 相對識別時間增長
                      算法實現和維護的工作量增加
                      紋理信息和人臉結構信息的混合[76, 80, 83, 98, 102]
                      人臉結構信息與運動生命信息的混合[89]
                      圖像質量與運動生命信息的混合[95]
                      背景信息[27]和其他特征的混合[79, 81, 84, 87, 93, 98, 100]
                      下載: 導出CSV

                      表  2  主流人臉活體檢測數據集總覽

                      Table  2  Brief overview of face anti-spoofing datasets

                      數據集 年份 假體人臉 個體數 數據量 姿態、表情、光照等錄制場景 錄制設備與圖像分辨率
                      NUAA[116] 2010 三種打印照片 15 12 641張圖像 三個不同光照的外界環境 網絡攝像頭–可見光圖像640 × 480像素
                      Yale Recaptured[33] 2011 LCD屏顯示的照片 10 2 560張圖像 64種不同光照 Kodak C813 8.2與Samsung Omnia i900的攝像頭–裁剪后的灰度圖 64 × 64像素
                      Print-Attack Database[117] 2011 手持照片、固定照片 50 200個視頻 兩種不同光照 可見光圖像
                      蘋果筆記本內置攝像頭– 320 × 240像素
                      CASIA MFSD[34] 2012 彎曲照片、挖眼照片、視頻 50 600個視頻 室內光照 可見光圖像
                      使用時間長的USB攝像頭– 640 × 480像素; 新USB攝像頭– 480 × 640像素; Sony NEX-5攝像頭– 1 920 × 1 080像素
                      Replay Attack[16] 2012 手持或者固定的照片與視頻 50 1 300個視頻 兩種不同光照 可見光圖像
                      蘋果筆記本內置攝像頭– 320 × 240像素
                      MSU MFSD[45] 2015 高分辨率照片與視頻 35 280個視頻 一個場景 可見光圖像
                      MacBook Air 13內置攝像頭– 640 × 480像素
                      Google Nexus 5前置攝像頭– 720 × 480像素
                      UVAD[31] 2015 6種設備拍攝的人臉視頻 404 17 076個視頻 不同背景光照的室內室外場景 索尼攝像頭–可見光圖像1 366 × 768像素
                      REPLAY MOBILE[118] 2016 高分辨率照片與視頻 40 1 200個視頻 五種不同光照 iPad Mini2 (iOS) 以及LG-G4
                      前置攝像頭–可見光圖像720 × 1 280像素
                      MSU USSA[119] 2016 高分辨率照片與視頻 1 000 9 000張 一個場景 可見光圖像
                      Google Nexus 5前置攝像頭– 1 280 × 960像素; 后置攝像頭– 3 264 × 2 448像素
                      Oulu-NPU[120] 2017 照片與視頻 55 5 940個視頻 三種不同光照場景 六種智能手機的前置攝像頭–可見光圖像1 920 × 1 080像素
                      SiW[89] 2018 高低兩種分辨率的照片, 彎曲照片與高分辨率視頻 165 4 478個視頻 活體人臉錄制了距離、姿態、表情、光照差異 Canon EOST6, Logitech C920攝像頭–可見光圖像1 920 × 1 080像素
                      GUC LiFFAD[21] 2015 激光、噴墨打印的照片, iPad顯示的照片 80 4 826張圖像 不同焦距的圖像, 室內室外場景 光場相機
                      Msspoof[121] 2016 可見光與近紅外光譜的黑白照片 22 4 704張圖片 7種不同的室內室外環境 uEye攝像頭以及近紅外濾波器
                      可見光與近紅外圖像– 1 280 × 1 024像素
                      EMSPAD[122] 2017 激光打印的照片, 噴墨打印的照片 50 10 500張圖像 2個場景 多光譜攝像頭7個波段的圖像
                      裁剪對齊后120 × 120像素
                      3DMAD[37] 2013 定制三維人臉面具 17 76 500張圖像 3種不同場景 Kinect深度攝像頭–深度圖 640 × 480像素; 可見光攝像頭–可見光圖像640 × 480像素
                      HKBU MARsV2[123] 2016 兩種三維人臉面具 12 1 008個視頻 7種不同光照 可見光圖像, 三種傳統攝像頭:
                      Logitech C920網絡攝像頭– 1 280 × 720像素; 工業攝像頭– 800 × 600像素; Canon EOS M3-1 280 × 720像素; 可見光圖像, 4種移動設備攝像頭:
                      Nexus 5, iPhone6, Samsung S7, Sony Tablet S;
                      SMAD[92] 2017 硅膠三維人臉面具 130個視頻 不同光照, 不同錄制背景環境
                      MLFP[124] 2017 挖去眼部的二維照片, 乳膠三維人臉面具 10 1 350個視頻 室內室外場景 Android智能手機–可見光圖像1 280 × 720像素; FLIR ONE熱像儀安卓版–熱紅外圖像640 × 480像素; 微軟Kinect –近紅外圖像424 × 512像素
                      下載: 導出CSV

                      表  3  CASIA-MFSD與Replay-Attack數據集單數據集測試性能數據(%)

                      Table  3  The performance of intra-test on CASIA-MFSD and Replay-Attack datasets (%)

                      方法 CASIA-MFSD Replay-Attack
                      EER EER HTER
                      LBP[16] 18.2 13.9 13.8
                      DoG[34] 17.0
                      Motion Magn[55] 14.4 0.0 1.25
                      IDA[43] 32.4 15.2
                      LBP-TOP[17] 10.0 7.9 7.6
                      CNN[79] 7.4 6.1 2.1
                      DMD + LBP[56] 21.8 5.3 3.8
                      IDA and motion[95] 5.8 0.83 0.0
                      Colour LBP[20] 2.1 0.4 2.8
                      VLBC[127] 6.5 1.7 0.8
                      3D CNN[94] 5.2 0.16 0.04
                      FD-ML-LPQ-FS[41] 4.6 5.6 4.8
                      patch + depthCNN[80] 2.7 0.8 0.7
                      SURF[39] 2.8 0.1 2.2
                      PreDRS + LSTM[84] 1.22 1.03 1.18
                      ST Mapping[82] 1.1 0.78 0.80
                      DDGL[92] 1.3 0.0
                      LiveNet[88] 4.59 5.74
                      Color texture[35] 4.6 1.2 4.2
                      DSGN[90] 3.42 0.13 0.63
                      deep LBP[85] 2.3 0.1 0.9
                      3D CNN + geneloss[93] 1.4 0.3 1.2
                      SSD + SPMT[98] 0.04 0.04 0.06
                      下載: 導出CSV

                      表  4  Oulu數據集單數據集測試性能數據(%)

                      Table  4  The performance of intra-test on Oulu dataset (%)

                      協議 方法 APCER BPCER ACER
                      1 GRADIANTex[128] 7.1 5.8 6.5
                      1 CPq[128] 2.9 10.08 6.9
                      1 GRADIANT[128] 1.3 12.5 6.9
                      1 Auxiliary[89] 1.6 1.6 1.6
                      1 Noise Modeling[129] 1.2 1.7 1.5
                      1 TDI[130] 2.5 0.0 1.3
                      2 GRADIANT[128] 3.1 1.9 2.5
                      2 GRADIANTex[128] 6.9 2.5 4.7
                      2 MixedFASNet[128] 9.7 2.5 6.1
                      2 Auxiliary[89] 2.7 2.7 2.7
                      2 Noise Modeling[129] 4.2 4.4 4.3
                      2 TDI[130] 1.7 2.0 1.9
                      3 GRADIANT[128] 2.6±3.9 5.0±5.3 3.8±2.4
                      3 GRADIANTex[128] 2.4±2.8 5.6±4.3 4.0±1.9
                      3 MixedFASNet[128] 5.3±6.7 7.8±5.5 6.5±4.6
                      3 Auxiliary[89] 2.7±1.3 3.1±1.7 2.9±1.5
                      3 Noise Modeling[129] 4.0±1.8 3.8±1.2 3.6±1.6
                      3 TDI[130] 5.9±1.0 5.9±1.0 5.9±1.0
                      4 GRADIANT[128] 5.0±4.5 15.0±7.1 10.0±5.0
                      4 GRADIANTex[128] 27.5±24.2 3.3±4.1 15.4±11.8
                      4 Massy HNU[128] 35.8±35.3 8.3±4.1 22.1±17.6
                      4 Auxiliary[89] 9.3±5.6 10.4±6.0 9.5±6.0
                      4 Noise Modeling[129] 5.1±6.3 6.1±5.1 5.6±5.7
                      4 TDI[130] 14.0±3.4 4.1±3.4 9.2±3.4
                      下載: 導出CSV

                      表  5  SiW數據集單數據集測試性能數據(%)

                      Table  5  The performance of intra-test on SiW dataset (%)

                      評價協議 方法 APCER BPCER ACER
                      1 Auxiliary[89] 3.58 3.58 3.58
                      1 TDI[130] 0.96 0.50 0.73
                      2 Auxiliary[89] 0.57±0.69 0.57±0.69 0.57±0.69
                      2 TDI[130] 0.08±0.14 0.21±0.14 0.15±0.14
                      3 Auxiliary[89] 8.31±3.81 8.31±3.80 8.31±3.81
                      3 TDI[130] 3.10±0.81 3.09±0.81 3.10±0.81
                      下載: 導出CSV

                      表  6  3DMAD、SMAD與HKBU-MARsV2數據集單數據集測試性能數據(%)

                      Table  6  The performance of intra-test on 3DMAD, SMAD and HKBU-MARsV2 datasets (%)

                      方法 3DMAD SMAD HKBU-MARsV2
                      HTER HTER EER HTER
                      LBPs[38] 0.1 20.8 22.5 24.0±25.6
                      deep and color[37] 0.95
                      IDA motion[95] 0
                      Color texcure[20] 23.0 23.4±20.5
                      videolet agg[101] 0 20.4
                      GrPPG[57] 7.94 16.4 16.1±20.5
                      LBP-TOP[92] 21.5
                      DBN[92] 0.5 19.2
                      DDGL[92] 0 13.1
                      CFrPPG[60] 6.82±12.1 4.04 4.42±5.1
                      下載: 導出CSV

                      表  7  CASIA-MFSD與Replay-Attack數據集間跨數據集測試性能數據HTER (%)

                      Table  7  The performance of inter-test between CASIA-MFSD and Replay-Attack (%)

                      訓練 CASIA-MFSD Replay-Attack
                      測試 Replay-Attack CASIA-MFSD
                      LBP[126] 55.9 57.6
                      Motion[126] 50.2 47.9
                      Motion Magn[55] 50.1 47.0
                      LBP-TOP[126] 49.7 60.6
                      CNN[79] 48.5 45.5
                      Color LBP[20] 30.3 37.7
                      texture+Motion[99] 12.4 31.6
                      FD-ML-LPQ-FS[41] 50.25 42.59
                      ST Mapping[82] 35.05 40.22
                      SURF[39] 26.9 23.2
                      DDGL[92] 22.8 27.4
                      Noise Modeling[129] 28.5 41.1
                      DeepImg+rPPG[89] 27.6 28.4
                      Domain Adapt[91] 27.4 36.0
                      Color texture[35] 9.6 39.2
                      LiveNet[88] 8.39 19.12
                      下載: 導出CSV

                      表  8  3DMAD與HKBU-MARsV2數據集間跨數據集測試性能數據HTER (%)

                      Table  8  The performance of inter-test between 3DMAD and HKBU-MARsV2 (%)

                      訓練 3DMAD HKBU-MARsV2
                      測試 HKBU-MARsV2 3DMAD
                      Color texcure[20] 40.1±7.8 47.7±5.4
                      LBPs[38] 53.0±3.6 32.8±11.5
                      pretrain CNN[60] 50.0±0.0 50.0±0.0
                      GrPPG[57] 24.3±7.1 15.7±6.8
                      CFrPPG[60] 2.51±0.1 2.55±0.1
                      下載: 導出CSV
                      360彩票
                    • [1] 中華人民共和國公安部. 安防人臉識別應用防假體攻擊測試方法, GA/T 1212-2014, 2014.

                      Ministry of Public Security of the People's Republic of China. Face Recognition Applications in Security Systems-Testing Methodologies for Anti-Spoofing, GA/T 1212-2014, 2014.
                      [2] Li Y, Xu K, Yan Q, Li Y J, Deng R H. Understanding OSN-based facial disclosure against face authentication systems. In: Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security. Kyoto, Japan: ACM, 2014. 413-424
                      [3] Chakraborty S, Das D. An overview of face liveness detection. International Journal on Information Theory, 2014, 3(2): Article No. 2
                      [4] Souza L, Oliveira L, Pamplona M, Papa J. How far did we get in face spoofing detection? Engineering Applications of Artificial Intelligence, 2018, 72: 368-381 doi: 10.1016/j.engappai.2018.04.013
                      [5] Ramachandra R, Busch C. Presentation attack detection methods for face recognition systems: A comprehensive survey. ACM Computing Surveys, 2017, 50(1): Article No. 8
                      [6] 鄭河榮, 褚一平, 潘翔, 趙小敏. 基于人臉姿態控制的交互式視頻活體檢測方法及其系統. CN 201510764681, 中國, 2016-01-20

                      Zheng He-Rong, Chu Yi-Ping, Pan Xiang, Zhao Xiao-Min. Interactive Video in Vivo Detection Method Based on Face Attitude Control and System Thereof. CN Patent 201510764681, China, January 20, 2016
                      [7] 薛炳如, 卜習栓, 王金鳳. 一種基于動作識別的活體人臉識別方法及系統. CN 201611129097, 中國, 2017-05-10

                      Xue Bing-Ru, Bu Xi-Shuan, Wang Jin-Feng. Action Recognition Based Living Body Face Recognition Method and System. CN Patent 201611129097, China, May 10, 2017
                      [8] 王先基, 陳友斌. 一種活體人臉檢測方法與系統. CN 201310384 572, 中國, 2013-12-11

                      Wang Xian-Ji, Chen You-Bin. Method and System for Detecting Living Body Human Face. CN Patent 2013103845 72, China, December 11, 2013
                      [9] 徐光柱, 劉鳴, 尹潘龍, 雷幫軍, 李春林. 基于人眼區域活動狀態的活體檢測方法和裝置. CN 201510472931, 中國, 2015-12-09

                      Xu Guang-Zhu, Liu Ming, Yin Pan-Long, Lei Bang-Jun, Li Chun-Lin. Living Body Detection Method and Apparatus Based on Active State of Human Eye Region. CN Patent 201510472931, China, December 9, 2015
                      [10] 汪鋮杰, 李季檁, 倪輝, 吳永堅, 黃飛躍. 人臉識別方法及識別系統. CN 201510319470, 中國, 2015-10-07

                      Wang Cheng-Jie, Li Ji-Lin, Ni Hui, Wu Yong-Jian, Huang Fei-Yue. Face Recognition Method and Recognition System. CN Patent 201510319470, China, October 7, 2015
                      [11] Kollreider K, Fronthaler H, Faraj M I, Bigun J. Real-time face detection and motion analysis with application in "liveness" assessment. IEEE Transactions on Information Forensics and Security, 2007, 2(3): 548-558 doi: 10.1109/TIFS.2007.902037
                      [12] Ng E S, Chia Y S. Face verification using temporal affective cues. In: Proceedings of the 21st International Conference on Pattern Recognition. Tsukuba, Japan: IEEE, 2012. 1249-1252
                      [13] Chetty G, Wagner M. Liveness verification in audio-video authentication. In: Proceedings of the 10th Australian International Conference on Speech Science and Technology. Sydney, Australia: Australian Speech Science and Technology Association Inc, 2004. 358-363
                      [14] Frischholz R W, Werner A. Avoiding replay-attacks in a face recognition system using head-pose estimation. In: Proceedings of the 2013 IEEE International SOI Conference. Nice, France: IEEE, 2003. 234-235
                      [15] M??tt? J, Hadid A, Pietik?inen M. Face spoofing detection from single images using micro-texture analysis. In: Proceedings of the 2011 International Joint Conference on Biometrics. Washington, USA: IEEE, 2011. 1-7
                      [16] Chingovska I, Anjos A, Marcel S. On the effectiveness of local binary patterns in face anti-spoofing. In: Proceedings of the 11th International Conference of Biometrics Special Interest Group. Darmstadt, Germany: IEEE, 2012. 1-7
                      [17] De Freitas Pereira T, Komulainen J, Anjos A, De Martino J M, Hadid A, Pietik?inen M, et al. Face liveness detection using dynamic texture. EURASIP Journal on Image and Video Processing, 2014, 2014(1): Article No. 2
                      [18] De Freitas Pereira T, Anjos A, De Martino J M, Marcel S. LBP-TOP based countermeasure against face spoofing attacks. In: Proceedings of the 2012 Asian Conference on Computer Vision. Daejeon, Korea (South): Springer, 2012. 121-132
                      [19] Komulainen J, Hadid A, Pietik?ainen M. Face spoofing detection using dynamic texture. In: Proceedings of the 2012 Asian Conference on Computer Vision. Daejeon, Korea (South): Springer, 2012. 146-157
                      [20] Boulkenafet Z, Komulainen J, Hadid A. Face spoofing detection using colour texture analysis. IEEE Transactions on Information Forensics and Security, 2016, 11(8): 1818-1830 doi: 10.1109/TIFS.2016.2555286
                      [21] Raghavendra R, Raja K B, Busch C. Presentation attack detection for face recognition using light field camera. IEEE Transactions on Image Processing, 2015, 24(3): 1060-1075 doi: 10.1109/TIP.2015.2395951
                      [22] Kose N, Dugelay J L. Classification of captured and recaptured images to detect photograph spoofing. In: Proceedings of the 2012 International Conference on Informatics, Electronics and Vision. Dhaka, Bangladesh: IEEE, 2012. 1027-1032
                      [23] Yang J W, Lei Z, Liao S C, Li S Z. Face liveness detection with component dependent descriptor. In: Proceedings of the 2013 International Conference on Biometrics. Madrid, Spain: IEEE, 2013. 1-6
                      [24] Raghavendra R, Busch C. Robust 2D/3D face mask presentation attack detection scheme by exploring multiple features and comparison score level fusion. In: Proceedings of the 17th International Conference on Information Fusion. Salamanca, Spain: IEEE, 2014. 1-7
                      [25] Arashloo S R, Kittler J, Christmas W. Face spoofing detection based on multiple descriptor fusion using multiscale dynamic binarized statistical image features. IEEE Transactions on Information Forensics and Security, 2015, 10(11): 2396-2407 doi: 10.1109/TIFS.2015.2458700
                      [26] M??tt? J, Hadid A, Pietik?inen M. Face spoofing detection from single images using texture and local shape analysis. IET Biometrics, 2012, 1(1): 3-10 doi: 10.1049/iet-bmt.2011.0009
                      [27] Komulainen J, Hadid A, Pietik?inen M. Context based face anti-spoofing. In: Proceedings of the 6th IEEE International Conference on Biometrics: Theory, Applications and Systems. Arlington, USA: IEEE, 2013. 1-8
                      [28] Schwartz W R, Rocha A, Pedrini H. Face spoofing detection through partial least squares and low-level descriptors. In: Proceedings of the 2011 International Joint Conference on Biometrics. Washington, USA: IEEE, 2011. 1-8
                      [29] Yang J W, Lei Z, Yi D, Li S Z. Person-specific face antispoofing with subject domain adaptation. IEEE Transactions on Information Forensics and Security, 2015, 10(4): 797-809 doi: 10.1109/TIFS.2015.2403306
                      [30] Da Silva Pinto A, Pedrini H, Schwartz W, Rocha A. Video-based face spoofing detection through visual rhythm analysis. In: Proceedings of the 25th SIBGRAPI Conference on Graphics, Patterns and Images. Ouro Preto, Brazil: IEEE, 2012. 221-228
                      [31] Pinto A, Schwartz W R, Pedrini H, De Rezende Rocha A. Using visual rhythms for detecting video-based facial spoof attacks. IEEE Transactions on Information Forensics and Security, 2015, 10(5): 1025-1038 doi: 10.1109/TIFS.2015.2395139
                      [32] Waris M A, Zhang H L, Ahmad I, Kiranyaz S, Gabbouj M. Analysis of textural features for face biometric anti-spoofing. In: Proceedings of the 21st European Signal Processing Conference. Marrakech, Morocco: IEEE, 2013. 1-5
                      [33] Peixoto B, Michelassi C, Rocha A. Face liveness detection under bad illumination conditions. In: Proceedings of the 18th IEEE International Conference on Image Processing. Brussels, Australia: IEEE, 2011. 3557-3560
                      [34] Zhang Z W, Yan J J, Liu S F, Lei Z, Yi D, Li S Z. A face antispoofing database with diverse attacks. In: Proceedings of the 5th IARR International Conference on Biometrics. New Delhi, India: IEEE, 2012. 26-31
                      [35] Boulkenafet Z, Komulainen J, Hadid A. On the generalization of color texture-based face anti-spoofing. Image and Vision Computing, 2018, 77: 1-9 doi: 10.1016/j.imavis.2018.04.007
                      [36] Kose N, Dugelay J L. Countermeasure for the protection of face recognition systems against mask attacks. In: Proceedings of the 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition. Shanghai, China: IEEE, 2013. 1-6
                      [37] Erdogmus N, Marcel S. Spoofing in 2D face recognition with 3D masks and anti-spoofing with kinect. In: Proceedings of the 6th IEEE International Conference on Biometrics: Theory, Applications and Systems. Arlington, USA: IEEE, 2013. 1-6
                      [38] Erdogmus N, Marcel S. Spoofing face recognition with 3D masks. IEEE Transactions on Information Forensics and Security, 2014, 9(7): 1084-1097 doi: 10.1109/TIFS.2014.2322255
                      [39] Boulkenafet Z, Komulainen J, Hadid A. Face antispoofing using speeded-up robust features and fisher vector encoding. IEEE Signal Processing Letters, 2017, 24(2): 141-145
                      [40] Chan P P K, Liu W W, Chen D N, Yeung D S, Zhang F, Wang X Z, et al. Face liveness detection using a flash against 2D spoofing attack. IEEE Transactions on Information Forensics and Security, 2018, 13(2): 521-534 doi: 10.1109/TIFS.2017.2758748
                      [41] Benlamoudi A, Aiadi K E, Ouafi A, Samai D, Oussalah M. Face antispoofing based on frame difference and multilevel representation. Journal of Electronic Imaging, 2017, 26(4): Article No. 043007
                      [42] Mohan K, Chandrasekhar P, Jilani S A K. Object face liveness detection with combined HOGlocal phase quantization using fuzzy based SVM classifier. Indian Journal of Science and Technology, 2017, 10(3): 1-10
                      [43] Galbally J, Marcel S, Fierrez J. Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition. IEEE Transactions on Image Processing, 2014, 23(2): 710-724 doi: 10.1109/TIP.2013.2292332
                      [44] Galbally J, Marcel S. Face anti-spoofing based on general image quality assessment. In: Proceedings of the 22nd International Conference on Pattern Recognition. Stockholm, Sweden: IEEE, 2014. 1173-1178
                      [45] Wen D, Han H, Jain A K. Face spoof detection with image distortion analysis. IEEE Transactions on Information Forensics and Security, 2015, 10(4): 746-761 doi: 10.1109/TIFS.2015.2400395
                      [46] Li H L, Wang S Q, Kot A C. Face spoofing detection with image quality regression. In: Proceedings of the 6th International Conference on Image Processing Theory, Tools and Applications. Oulu, Finland: IEEE, 2016. 1-6
                      [47] Akhtar Z, Foresti G L. Face spoof attack recognition using discriminative image patches. Journal of Electrical and Computer Engineering, 2016, 2016: Article No. 4721849
                      [48] Pan G, Sun L, Wu Z H, Lao S H. Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: Proceedings of the 11th IEEE International Conference on Computer Vision. Rio de Janeiro, Brazil: IEEE, 2007. 1-8
                      [49] Sun L, Pan G, Wu Z H, Lao S H. Blinking-based live face detection using conditional random fields. In: Proceedings of the 2007 International Conference on Biometrics. Seoul, Korea (South): Springer, 2007. 252-260
                      [50] Li J W. Eye blink detection based on multiple Gabor response waves. In: Proceedings of the 2008 International Conference on Machine Learning and Cybernetics. Kunming, China: IEEE, 2008. 2852-2856
                      [51] Bharadwaj S, Dhamecha T I, Vatsa M, Singh R. Face Anti-Spoofing via Motion Magnification and Multifeature Videolet Aggregation, Technology Report, IIITD-TR2014-002, Indraprastha Institute of Information Technology, New Delhi, India, 2014.
                      [52] Bao W, Li H, Li N, Jiang W. A liveness detection method for face recognition based on optical flow field. In: Proceedings of the 2009 International Conference on Image Analysis and Signal Processing. Linhai, China: IEEE, 2009. 233 -236
                      [53] Kollreider K, Fronthaler H, Bigun J. Verifying liveness by multiple experts in face biometrics. In: Proceedings of the 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Anchorage, USA: IEEE, 2008. 1-6
                      [54] Kollreider K, Fronthaler H, Bigun J. Non-intrusive liveness detection by face images. Image and Vision Computing, 2009, 27(3): 233-244 doi: 10.1016/j.imavis.2007.05.004
                      [55] Bharadwaj S, Dhamecha T I, Vatsa M, Singh R. Computationally efficient face spoofing detection with motion magnification. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Portland, USA: IEEE, 2013. 105-110
                      [56] Tirunagari S, Poh N, Windridge D, Iorliam A, Suki N, Ho A T S. Detection of face spoofing using visual dynamics. IEEE Transactions on Information Forensics and Security, 2015, 10(4): 762-777 doi: 10.1109/TIFS.2015.2406533
                      [57] Li X B, Komulainen J, Zhao G Y, Yuen P C, Pietik?inen M. Generalized face anti-spoofing by detecting pulse from face videos. In: Proceedings of the 23rd International Conference on Pattern Recognition. Cancun, Mexico: IEEE, 2016. 4244-4249
                      [58] Nowara E M, Sabharwal A, Veeraraghavan A. PPGSecure: Biometric presentation attack detection using photopletysmograms. In: Proceedings of the 12th IEEE International Conference on Automatic Face & Gesture Recognition. Washington, USA: IEEE, 2017. 56-62
                      [59] Hernandez-Ortega J, Fierrez J, Morales A, Tome P. Time analysis of pulse-based face anti-spoofing in visible and NIR. In: Proceedings of the 2018 Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA: IEEE, 2018. 544-552
                      [60] Liu S Q, Lan X Y, Yuen P C. Remote photoplethysmography correspondence feature for 3D mask face presentation attack detection. In: Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer, 2018. 558-573
                      [61] Wang S Y, Yang S H, Chen Y P, Huang J W. Face liveness detection based on skin blood flow analysis. Symmetry, 2017, 9(12): Article No. 305
                      [62] Yi D, Lei Z, Zhang Z W, Li S Z. Face anti-spoofing: Multi-spectral approach. Handbook of Biometric Anti-Spoofing. London: Springer, 2014.
                      [63] Kim Y S, Na J, Yoon S, Yi J. Masked fake face detection using radiance measurements. Journal of the Optical Society of America A, 2009, 26(4): 760-766 doi: 10.1364/JOSAA.26.000760
                      [64] Zhang Z W, Yi D, Lei Z, Li S Z. Face liveness detection by learning multispectral reflectance distributions. In: Proceedings of the 2011 IEEE International Conference on Automatic Face and Gesture Recognition. Santa Barbara, CA, USA: IEEE, 2011. 436-441
                      [65] Sun X D, Huang L, Liu C P. Context based face spoofing detection using active near-infrared images. In: Proceedings of the 23rd International Conference on Pattern Recognition. Cancun, Mexico: IEEE, 2016. 4262-4267
                      [66] Sun X D, Huang L, Liu C P. Multispectral face spoofing detection using VIS-NIR imaging correlation. International Journal of Wavelets, Multiresolution and Information Processing, 2018, 16(2): Article No. 1840003
                      [67] Kose N, Dugelay J L. Reflectance analysis based countermeasure technique to detect face mask attacks. In: Proceedings of the 18th International Conference on Digital Signal Processing. Fira, Greece: IEEE, 2013. 1-6
                      [68] Dowdall J, Pavlidis I, Bebis G. Face detection in the near-IR spectrum. Image and Vision Computing, 2003, 21(7): 565-578 doi: 10.1016/S0262-8856(03)00055-6
                      [69] Steiner H, Kolb A, Jung N. Reliable face anti-spoofing using multispectral SWIR imaging. In: Proceedings of the 2016 International Conference on Biometrics. Halmstad, Sweden: IEEE, 2016. 1-8
                      [70] Kant C, Sharma N. Fake face recognition using fusion of thermal imaging and skin elasticity. International Journal of Computer Science and Communications, 2013, 4(1): 65 -72 http://pdfs.semanticscholar.org/83f7/9d370cbfaebeb363c4300ae61968ccf04bf8.pdf
                      [71] Raghavendra R, Raja K B, Marcel S, Busch C. Face presentation attack detection across spectrum using time-frequency descriptors of maximal response in laplacian scale-space. In: Proceedings of the 6th International Conference on Image Processing Theory, Tools, and Applications. Oulu, Finland: IEEE, 2016. 1-6
                      [72] Raghavendra R, Raja K B, Venkatesh S, Busch C. Face presentation attack detection by exploring spectral signatures. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu, USA: IEEE, 2017. 672-679
                      [73] Kim S, Ban Y, Lee S. Face liveness detection using a light field camera. Sensors, 2014, 14(12): 22471-22499 doi: 10.3390/s141222471
                      [74] Xie X H, Gao Y, Zheng W S, Lai J H, Zhu J Y. One-snapshot face anti-spoofing using a light field camera. In: Proceedings of the 12th Chinese Conference on Biometric Recognition. Shenzhen, China: Springer, 2017. 108-117
                      [75] Wang Y, Nian F D, Li T, Meng Z J, Wang K Q. Robust face anti-spoofing with depth information. Journal of Visual Communication and Image Representation, 2017, 49: 332-337 doi: 10.1016/j.jvcir.2017.09.002
                      [76] Raghavendra R, Busch C. Novel presentation attack detection algorithm for face recognition system: Application to 3D face mask attack. In: Proceedings of the 2014 IEEE International Conference on Image Processing. Paris, France: IEEE, 2014. 323-327
                      [77] Lagorio A, Tistarelli M, Cadoni M, Fookes C, Sridharan S. Liveness detection based on 3D face shape analysis. In: Proceedings of the 2013 International Workshop on Biometrics and Forensics. Lisbon, Portugal: IEEE, 2013. 1-4
                      [78] Tang Y H, Chen L M. 3D facial geometric attributes based anti-spoofing approach against mask attacks. In: Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition. Washington, DC, USA: IEEE, 2017. 589-595
                      [79] Yang J W, Lei Z, Li S Z. Learn convolutional neural network for face anti-spoofing. arXiv Preprint arXiv: 1408. 5601, 2014.
                      [80] Atoum Y, Liu Y J, Jourabloo A, Liu X M. Face anti-spoofing using patch and depth-based CNNs. In: Proceedings of the 2017 IEEE International Joint Conference on Biometrics. Denver, Colorado, USA: IEEE, 2017: 319-328
                      [81] Alotaibi A, Mahmood A. Deep face liveness detection based on nonlinear diffusion using convolution neural network. Signal, Image and Video Processing, 2017, 11(4): 713-720 doi: 10.1007/s11760-016-1014-2
                      [82] Lakshminarayana N N, Narayan N, Napp N, Setlur S, Govindaraju V. A discriminative spatio-temporal mapping of face for liveness detection. In: Proceedings of the 2017 IEEE International Conference on Identity, Security and Behavior Analysis. New Delhi, India: IEEE, 2017. 1-7
                      [83] Li L, Feng X Y, Boulkenafet Z, Xia Z Q, Li M M, Hadid A. An original face anti-spoofing approach using partial convolutional neural network. In: Proceedings of the 6th International Conference on Image Processing Theory, Tools and Applications. Oulu, Finland: IEEE, 2016. 1-6
                      [84] Tu X K, Fang Y C. Ultra-deep neural network for face anti-spoofing. In: Proceedings of the 24th International Conference on Neural Information Processing. Guangzhou, China: Springer, 2017. 686-695
                      [85] Li L, Feng X Y, Jiang X Y, Xia Z Q, Hadid A. Face anti-spoofing via deep local binary patterns. In: Proceedings of the 2017 IEEE International Conference on Image Processing. Beijing, China: IEEE, 2017. 101-105
                      [86] Lucena O, Junior A, Moia V, Souza R, Valle E, Lotufo R. Transfer learning using convolutional neural networks for face anti-spoofing. In: Proceedings of the 14th International Conference Image Analysis and Recognition. Montreal, Canada: Springer, 2017. 27-34
                      [87] Nagpal C, Dubey S R. A performance evaluation of convolutional neural networks for face anti spoofing. arXiv Preprint arXiv: 1805.04176, 2018.
                      [88] Rehman Y A U, Po L M, Liu M Y. LiveNET: Improving features generalization for face liveness detection using convolution neural networks. Expert Systems with Applications, 2018, 108: 159-169 doi: 10.1016/j.eswa.2018.05.004
                      [89] Liu Y J, Jourabloo A, Liu X M. Learning deep models for face anti-spoofing: Binary or auxiliary supervision. In: Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 389-398
                      [90] Ning X, Li W J, Wei M L, Sun L J, Dong X L. Face anti-spoofing based on deep stack generalization networks. In: Proceedings of the 2018 International Conference on Pattern Recognition Applications and Methods. Funchal, Madeira, Portugal: SCITEPRESS, 2018.
                      [91] Li H L, Li W, Cao H, Wang S Q, Huang F Y, Kot A C. Unsupervised domain adaptation for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 2018, 13(7): 1794-1809 doi: 10.1109/TIFS.2018.2801312
                      [92] Manjani I, Tariyal S, Vatsa M, Singh R, Majumdar A. Detecting silicone mask-based presentation attack via deep dictionary learning. IEEE Transactions on Information Forensics and Security, 2017, 12(7): 1713-1723 doi: 10.1109/TIFS.2017.2676720
                      [93] Li H L, He P S, Wang S Q, Rocha A, Jiang X H, Kot A C. Learning generalized deep feature representation for face anti-spoofing. IEEE Transactions on Information Forensics and Security, 2018, 13(10): 2639-2652 doi: 10.1109/TIFS.2018.2825949
                      [94] Gan J Y, Li S L, Zhai Y K, Liu C Y. 3D convolutional neural network based on face anti-spoofing. In: Proceedings of the 2nd International Conference on Multimedia and Image Processing. Wuhan, China: IEEE, 2017. 1-5
                      [95] Feng L T, Po L M, Li Y M, Xu X Y, Yuan F, Cheung T C H, et al. Integration of image quality and motion cues for face anti-spoofing: A neural network approach. Journal of Visual Communication and Image Representation, 2016, 38: 451-460 doi: 10.1016/j.jvcir.2016.03.019
                      [96] Asim M, Ming Z, Javed M Y. CNN based spatio-temporal feature extraction for face anti-spoofing. In: Proceedings of the 2nd International Conference on Image, Vision and Computing. Chengdu, China: IEEE, 2017. 234-238
                      [97] Shao R, Lan X Y, Yuen P C. Deep convolutional dynamic texture learning with adaptive channel-discriminability for 3D mask face anti-spoofing. In: Proceedings of the 2017 IEEE International Joint Conference on Biometrics. Denver, Colorado, USA: IEEE, 2017. 748-755
                      [98] Song X, Zhao X, Fang L J, Lin T W. Discriminative representation combinations for accurate face spoofing detection. Pattern Recognition, 2019, 85: 220-231 doi: 10.1016/j.patcog.2018.08.019
                      [99] Patel K, Han H, Jain A K. Cross-database face antispoofing with robust feature representation. In: Proceedings of the 11th Chinese Conference on Biometric Recognition. Chengdu, China: Springer, 2016. 611-619
                      [100] Tronci R, Muntoni D, Fadda G, Pili M, Sirena N, Murgia G, et al. Fusion of multiple clues for photo-attack detection in face recognition systems. In: Proceedings of the 2011 International Joint Conference on Biometrics. Washington, DC, USA: IEEE, 2011. 1-6
                      [101] Siddiqui T A, Bharadwaj S, Dhamecha T I, Agarwal A, Vatsa M, Singh R, et al. Face anti-spoofing with multifeature videolet aggregation. In: Proceedings of the 23rd International Conference on Pattern Recognition. Cancun, Mexico: IEEE, 2016. 1035-1040
                      [102] Kose N, Dugelay J L. Mask spoofing in face recognition and countermeasures. Image and Vision Computing, 2014, 32(10): 779-789 doi: 10.1016/j.imavis.2014.06.003
                      [103] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987 doi: 10.1109/TPAMI.2002.1017623
                      [104] Haralick R M, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610-621 doi: 10.1109/TSMC.1973.4309314
                      [105] Poh M Z, McDuff D J, Picard R W. Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Transactions on Biomedical Engineering, 2011, 58(1): 7-11 doi: 10.1109/TBME.2010.2086456
                      [106] Adelson E H, Wang J Y A. Single lens stereo with a plenoptic camera. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 1(2): 99-106 http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1109/34.121783&rfr_id=trans/tg/2009/02/ttg2009020221.htm
                      [107] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639 doi: 10.1109/34.56205
                      [108] He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 770-778
                      [109] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735-1780 doi: 10.1162/neco.1997.9.8.1735
                      [110] Wolpert D H. Stacked generalization. Neural Networks, 1992, 5(2): 241-259 doi: 10.1016/S0893-6080(05)80023-1
                      [111] Ting K M, Witten I H. Stacked generalization: When does it work? In: Proceedings of the 15th International Joint Conference on Artifical Intelligence. San Francisco, USA: Morgan Kaufmann Publishers Inc, 1997. 866-871
                      [112] Tran D, Bourdev L, Fergus R, Torresani L, Paluri M. Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 4489-4497
                      [113] Parkhi O M, Vedaldi A, Zisserman A. Deep face recognition. In: Proceedings of the 2015 British Machine Vision Conference. Swansea, UK: BMVA Press, 2015. 41.1-41.12
                      [114] Zhao G Y, Pietikainen M. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6): 915-928 doi: 10.1109/TPAMI.2007.1110
                      [115] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Yang F C, et al. SSD: Single shot MultiBox detector. In: Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands: Springer, 2016. 21-37
                      [116] Tan X Y, Li Y, Liu J, Jiang L. Face liveness detection from a single image with sparse low rank bilinear discriminative model. In: Proceedings of the 11th European Conference on Computer Vision. Crete, Greece: Springer, 2010. 504-517
                      [117] Anjos A, Marcel S. Counter-measures to photo attacks in face recognition: A public database and a baseline. In: Proceedings of the 2011 International Joint Conference on Biometrics. Washington, DC, USA: IEEE, 2011. 1-7
                      [118] Costa-Pazo A, Bhattacharjee S, Vazquez-Fernandez E, Marcel S. The replay-mobile face presentation-attack database. In: Proceedings of the 2016 International Conference of the Biometrics Special Interest Group. Darmstadt, Germany: IEEE, 2016. 1-7
                      [119] Patel K, Han H, Jain A K. Secure face unlock: Spoof detection on smartphones. IEEE Transactions on Information Forensics and Security, 2016, 11(10): 2268-2283 doi: 10.1109/TIFS.2016.2578288
                      [120] Boulkenafet Z, Komulainen J, Li L, Feng X Y, Hadid A. OULU-NPU: A mobile face presentation attack database with real-world variations. In: Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition. Washington, DC, USA: IEEE, 2017. 612-618
                      [121] Chingovska I, Erdogmus N, Anjos A, Marcel S. Face recognition systems under spoofing attacks. Face Recognition Across the Imaging Spectrum. Cham: Springer, 2016.
                      [122] Raghavendra R, Raja K B, Venkatesh S, Cheikh F A, Busch C. On the vulnerability of extended multispectral face recognition systems towards presentation attacks. In: Proceedings of the 2017 IEEE International Conference on Identity, Security and Behavior Analysis. New Delhi, India: IEEE, 2017. 1-8
                      [123] Liu S Q, Yang B Y, Yuen P C, Zhao G Y. A 3D mask face anti-spoofing database with real world variations. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Las Vegas, USA: IEEE, 2016. 100-106
                      [124] Agarwal A, Yadav D, Kohli N, Singh R, Vatsa M, Noore A. Face presentation attack with latex masks in multispectral videos. In: Proceedings of the 2017 Computer Vision and Pattern Recognition Workshops. Honolulu, USA: IEEE, 2017. 275-283
                      [125] International Organization for Standardization. ISO/IEC 30107-3. Biometrics, Information Technology-Biometric Presentation Attack Detection — Part 1: Framework, 2016.
                      [126] De Freitas Pereira T, Anjos A, De Martino J M, Marcel S. Can face anti-spoofing countermeasures work in a real world scenario? In: Proceedings of the 2013 International Conference on Biometrics. Madrid, Spain: IEEE, 2013. 1-8
                      [127] Zhao X C, Lin Y P, Heikkil? J. Dynamic texture recognition using volume local binary count patterns with an application to 2D face spoofing detection. IEEE Transactions on Multimedia, 2018, 20(3): 552-566 doi: 10.1109/TMM.2017.2750415
                      [128] Boulkenafet Z, Komulainen J, Akhtar Z, Benlamoudi A, Samai D, Bekhouche S E, et al. A competition on generalized software-based face presentation attack detection in mobile scenarios. In: Proceedings of the 2017 IEEE International Joint Conference on Biometrics. Denver, Colorado, USA: IEEE, 2017. 688-696
                      [129] Jourabloo A, Liu Y J, Liu X M. Face de-spoofing: Anti-spoofing via noise modeling. In: Proceedings of the 15th European Conference on Computer Vision. Munich, Germany: Springer, 2018.
                      [130] Wang Z Z, Zhao C X, Qin Y X, Zhou Q S, Lei Z. Exploiting temporal and depth information for multi-frame face anti-spoofing. arXiv Preprint arXiv: 1811.05118, 2018.
                    • 加載中
                    圖(5) / 表(8)
                    計量
                    • 文章訪問數:  156
                    • HTML全文瀏覽量:  107
                    • PDF下載量:  109
                    • 被引次數: 0
                    出版歷程
                    • 收稿日期:  2018-12-12
                    • 錄用日期:  2019-04-19
                    • 刊出日期:  2021-08-20

                    目錄

                      /

                      返回文章
                      返回