Face Image Quality Quantitative Assessment for Forensic Identification of Human Images

With the development of the face recognition technology, the face recognition techniques are more and more applied in the scenario of the forensic science. Forensic identification of human images is a forensic activity for verifying whether the questioned and the known face images are the same ones. The one to one face verification technique can be well applied in the above application. Researching on the effect of face image quality on the performance of face verification systems in the application of the forensic identification of human images leads to the problem of face image quality assessment. Firstly, we discuss and analyze factors that affect the assessment of face image quality in forensic identification of human images. The factors consist of the age, expression, imaging angle, image quality and others, which will influence the performance of the face verification system. Then we propose a quantitative analysis method for the assessment of face image quality, which is relied on the verification performance of face verification systems. The effect of face images under specific conditions is studied. The face image quality under the specific factor condition is quantitatively scored according to the similarity quantification value between face images calculated by the face verification system. For the implement of the face verification system, the deep learning based face recognition method is used for objective evaluation of the face image quality. The results in the paper have shown the important significance of our proposed method for the objective evaluation of face image quality, and for the reasonable selection of face images in videos in the practical cases of the forensic identification of human images.