Impact of Demographics on Automated Criminal Tendency Detection from Facial Images

Abstract

An individual’s face can provide important insight about his personal traits like age, psychology, health, ethnicity, emotions, kinship, and much more. The biometric potentials of facial images make them an ideal tool for various forensic inferences. One such interesting area of research is the detection of criminal tendencies in people from their facial images. Several studies have proposed machine and deep learning-based solutions for this purpose. However, to the best of our knowledge, none have explicitly analyzed the impact of demographic attributes on the performance of such systems. In this paper, we provide an in-depth analysis to measure the impact of three important demographic properties i.e. age, gender, and ethnicity on facial image-based criminality detection systems. For this purpose, a balanced dataset is prepared as there was no such dataset available with age, race, and gender splits. The performance of various convolutional neural network architectures (VGG-16, VGG-19, and FaceNet) is evaluated to assess their potential in perceiving criminal tendencies. Based on the outstanding performance of FaceNet, it is selected to measure the impact of different demographic groups in detecting criminal tendencies from facial images. The analysis presented in this study can prove vital for the development of robust and unbiased systems that can provide reliable proactive solutions for the security of all communities.