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                    弱對齊的跨光譜人臉檢測

                    閆夢凱 錢建軍 楊健

                    閆夢凱, 錢建軍, 楊健. 弱對齊的跨光譜人臉檢測. 自動化學報, 2021, x(x): 1?13 doi: 10.16383/j.aas.c210058
                    引用本文: 閆夢凱, 錢建軍, 楊健. 弱對齊的跨光譜人臉檢測. 自動化學報, 2021, x(x): 1?13 doi: 10.16383/j.aas.c210058
                    Yan Meng-Kai, Qian Jian-Jun, Yang Jian. Weakly aligned cross-spectral face detection. Acta Automatica Sinica, 2021, x(x): 1?13 doi: 10.16383/j.aas.c210058
                    Citation: Yan Meng-Kai, Qian Jian-Jun, Yang Jian. Weakly aligned cross-spectral face detection. Acta Automatica Sinica, 2021, x(x): 1?13 doi: 10.16383/j.aas.c210058

                    弱對齊的跨光譜人臉檢測

                    doi: 10.16383/j.aas.c210058
                    基金項目: 國家自然科學基金(61876083), 國家自然科學基金聯合基金(U1713208)資助
                    詳細信息
                      作者簡介:

                      閆夢凱:南京理工大學計算機科學與工程學院博士研究生, 主要研究方向為生物生理信息測量, 計算機視覺. E-mail: ymk@njust.edu.cn

                      錢建軍:南京理工大學計算機科學與工程學院副教授. 2014年獲得南京理工大學博士學位. 主要研究方向為模式識別, 計算機視覺. 本文通信作者. E-mail: csjqian@njust.edu.cn

                      楊?。耗暇├砉ご髮W計算機科學與工程學院教授. 2002年獲得南京理工大學博士學位. 主要研究方向為模式識別, 計算機視覺, 機器學習. E-mail: csjyang@njust.edu.cn

                    Weakly Aligned Cross-spectral Face Detection

                    Funds: Supported by National Natural Science Foundation of P. R. China (61876083), and National Science Fund of China under Grant(U1713208)
                    More Information
                      Author Bio:

                      YAN Meng-Kai Ph. D. candidate at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. His research interest covers Biophysiological information measurement and computer vision

                      QIAN Jian-Jun Associate Professor at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. He received his Ph. D. degree from Nanjing University of Science and Technology, China in 2014. His research interest covers pattern recognition and computer vision. Corresponding author of this paper

                      YANG Jian Professor at School of Computer Science and Engineering, Nanjing University of Science and Technology, China. He received his Ph. D. degree from Nanjing University of Science and Technology, China in 2002. His research interest covers pattern recognition, computer vision and machine learning

                    • 摘要: 跨光譜人臉檢測在活體人臉識別、體溫篩查等領域有著重要的應用價值. 眾所周知, 可見光人臉易于檢測, 然而紅外人臉難于檢測, 因此借助可見光圖像的人臉檢測結果進而完成紅外人臉檢測是一種有效的解決方案. 但是跨光譜圖像之間不可避免的存在偏差, 導致檢測精度不高. 為了解決這一問題, 本文提出了一種弱對齊跨光譜圖像的人臉檢測算法, 該方法基于跨光譜圖像之間的偏差設計了候選框布置策略, 并在此基礎上提出了跨光譜特征表示方法用于選取最優候選框. 此外, 本文還構建了一個跨光譜人臉數據集(Cross-spectrum face簡稱為CSF). 最后, 在CSF和OTCBVS (OTCBVS Benchmark dataset collection)人臉數據集上的實驗結果證明, 本文的方法能夠較好地完成紅外圖像人臉檢測任務.
                    • 圖  1  跨光譜人臉檢測

                      Fig.  1  Cross-spectral face detection

                      圖  2  雙相機與空間內任意一點的關系

                      Fig.  2  The relationship between dual cameras and any point in space

                      圖  3  空間中任意一點在相機中的成像坐標

                      Fig.  3  The imaging coordinates of any point in space in the camera

                      圖  4  像素坐標系與圖像坐標系的關系

                      Fig.  4  The relationship between pixel coordinate system and image coordinate system

                      圖  5  不同深度下的跨光譜人臉圖像

                      Fig.  5  Cross-spectral face images at different depths

                      圖  6  含有運動目標的跨光譜人臉圖像

                      Fig.  6  Cross-spectral face images with moving target

                      圖  7  人臉高度與其成像高度的關系

                      Fig.  7  Relationship between face height and image height

                      圖  8  跨光譜人臉檢測框架

                      Fig.  8  Cross-spectral face detection framework

                      圖  9  跨光譜特征表示網絡

                      Fig.  9  Cross-spectral feature representation network

                      圖  10  跨光譜特征表示網絡訓練方式

                      Fig.  10  Cross-spectral feature representation network training method

                      圖  11  含有部分人臉的負樣本

                      Fig.  11  Improved negative sample selection method

                      圖  12  相機安裝位置

                      Fig.  12  Camera installation location

                      圖  13  不同采集條件下的圖像

                      Fig.  13  Images under different acquisition conditions

                      圖  14  檢測結果對比圖

                      Fig.  14  Comparison of face detection results

                      表  1  測試集為CSF-白天的實驗結果

                      Table  1  The experment results on CSF-day

                      算法AP(%) Iou>0.5AP(%) Iou>0.3
                      坐標映射44.688.4
                      粗略糾正55.987.9
                      本文算法87.589.6
                      下載: 導出CSV

                      表  2  測試集為CSF-夜間的實驗結果

                      Table  2  The experment results on CSF- night

                      算法AP(%) Iou>0.5AP(%) Iou>0.3
                      坐標映射36.982.7
                      粗略糾正50.882.4
                      本文算法81.884.1
                      下載: 導出CSV

                      表  3  測試集為OTCBVS的實驗結果

                      Table  3  The experment results on OTCBVS

                      算法AP(%) Iou>0.5AP(%) Iou>0.3
                      坐標映射16.446.8
                      粗略糾正54.576.5
                      本文算法74.486.6
                      下載: 導出CSV

                      表  4  候選框召回率

                      Table  4  Proposal recall(%)

                      數據集Iou>0.5Iou>0.3
                      CSF96.998.4
                      OTCBVS89.391.6
                      下載: 導出CSV

                      表  5  CSF中候選框的選取對模型的影響

                      Table  5  The influence of the selection of the proposal on the model in CSF

                      候選框AP(Iou>0.5)時間(ms)
                      1/871.49
                      1/8,2/884.816
                      1/8,2/8,3/886.323
                      1/8,…,4/886.428
                      下載: 導出CSV

                      表  6  OTCBVS中候選框的選取對模型的影響

                      Table  6  The influence of the selection of the proposal on the model in OTCBVS

                      候選框AP(Iou>0.5)時間(ms)
                      1/870.510
                      1/8,2/873.116
                      1/8,2/8,3/874.224
                      1/8,…,4/874.430
                      下載: 導出CSV

                      表  7  負樣本類型對模型精度的影響

                      Table  7  Effect of negative sample type on model accuracy

                      負樣本類型AP(%) Iou>0.5
                      046.1
                      4/870.5
                      5/869.7
                      6/847.1
                      7/821.5
                      0,4/8,5/8,6/8,7/886.4
                      下載: 導出CSV

                      表  8  CSF數據集上的對比實驗結果

                      Table  8  Comparative experiment results on CSF dataset

                      算法AP(%) Iou>0.5AP(%) Iou>0.3
                      FaceBoxes9.19.1
                      S3FD9.19.1
                      Pyramidbox35.936.3
                      DSFD35.836.3
                      Tinyface56.582.4
                      S3FD-IR72.373.4
                      DSFD-IR81.983.7
                      DSFD-本文算法86.488.5
                      下載: 導出CSV

                      表  9  OTCBVS數據集上的對比實驗結果

                      Table  9  Comparative experiment results on OTCBVS dataset

                      算法AP(%) Iou > 0.5AP(%) Iou > 0.3
                      FaceBoxes--
                      S3FD9.19.1
                      Pyramidbox36.136.1
                      DSFD27.227.2
                      Tinyface25.138.6
                      S3FD-IR60.873.6
                      DSFD-IR69.470.4
                      DSFD-本文算法75.086.3
                      下載: 導出CSV
                      360彩票
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