弱对齐的跨光谱人脸检测

Weakly Aligned Cross-spectral Face Detection

跨光谱人脸检测在活体人脸识别、体温筛查等领域有着重要的应用价值.众所周知,可见光人脸易于检测,然而红外人脸难于检测,因此借助可见光图像的人脸检测结果进而完成红外人脸检测是一种有效的解决方案.但是跨光谱图像之间不可避免的存在偏差,导致检测精度不高.为了解决这一问题,提出了一种弱对齐跨光谱图像的人脸检测算法,该方法基于跨光谱图像之间的偏差设计了候选框布置策略,并在此基础上提出了跨光谱特征表示方法用于选取最优候选框.此外,本文还构建了一个跨光谱人脸数据集.最后,在跨光谱人脸数据集和OTCBVS人脸数据集上的实验结果证明,该方法能够较好地完成红外图像人脸检测任务.

Cross-spectral face detection has important application value in the field of face spoof detection and body temperature screening.It is known that it is easy to detect faces in visible light images.However,it is a challenge problem to localize faces accurately in infrared images.Therefore,it is an effective solution to complete infrared face detection based on the face detection results of visible light images.The result of above scheme is nevertheless unsatisfactory since there are unavoidable deviations between cross-spectral images.To solve this problem,this paper proposes a novel face detection algorithm based on weakly aligns cross-spectral images.This method designs the dispatch scheme of candidate box based on the deviation between cross-spectral images,and thus proposes a crossspectral feature representation method to select the best candidate box.In addition,this paper also constructed a cross-spectral face dataset.Finally,the experimental results on the cross-spectral face and OTCBVS face datasets demontrate that the proposed method can complete the task of infrared image face detection well over the competed methods.