一、讲座名称：Automated Inference on Criminality using Face Images

二、讲座时间：2016年11月25日（星期五）下午2:00

三、讲座地点：js33333金沙线路检测四楼报告厅

四、主讲人简介

Xiaolin Wu, Ph.D. in computer science, University of Calgary, Canada, 1988. Dr. Wu started his academic career in 1988, and has since been on the faculty of University of Western Ontario, New York Polytechnic University (NYU Poly), and currently McMaster University, where he is a professor at the Department of Electrical & Computer Engineering and holds the NSERC senior industrial research chair in Digital Cinema. His research interests include image processing, multimedia signal coding and communication, joint source-channel coding, multiple description coding, and network-aware visual communication. He has published over three hundred research papers and holds five patents in these fields. Dr. Wu is an IEEE fellow, a past associated editor of IEEE Transactions on Image Processing and IEEE Transactions on Multimedia, and served on the technical committees of many IEEE international conferences/workshops. Dr. Wu received numerous international awards and honors.

五、讲座内容简介

We study, for the first time, automated inference on criminality based solely on still face images, which is free of any biases of subjective judgments of human observers.Via supervised machine learning, we build four classifiers(logistic regression, KNN, SVM, CNN) using facial images of 1856 real persons controlled for race, gender, age and facial expressions, nearly half of whom were convicted criminals, for discriminating between criminals and noncriminals.All four classifiers perform consistently well and empirically establish the validity of automated face-induced inference on criminality, despite the historical controversy surrounding this line of enquiry. Also, some discriminating structural features for predicting criminality have been found by machine learning. Above all, the most important discovery of this research is that criminal and non-criminal face images populate two quite distinctive manifolds. The variation among criminal faces is significantly greater than that of the non-criminal faces. The two manifolds consisting of criminal and non-criminal faces appear to be concentric,with the non-criminal manifold lying in the kernel with a smaller span, exhibiting a law of ”normality” for faces of non-criminals. In other words, the faces of general law-biding public have a greater degree of resemblance compared with the faces of criminals, or criminals have a higher degree of dissimilarity in facial appearance than non-criminals.