Reliable Face Identification System for Criminal Investigation

Face identification technology has the potential to revolutionize criminal investigation by offering reliable and fast ways of identifying criminals. Although there is significant advancement in face recognition technology, adoption of this technology in law enforcement agencies is at infancy because of factors like system reliability and complexity. In this study, we propose a reliable face identification system (RFIS) for criminal investigation; a novel multi-faced approach for facial identification that identifies suspects by comparing live image, still picture and video obtained from various sources. The proposed system is developed by using two prominent libraries called Dlib and Haar Cascade because of their effectiveness. In addition, RFIS uses Convolutional Neural Network (CNN) architecture as the facial recognition algorithm which is trained on a dataset of criminal facial images to learn the features that distinguish a criminal’s face from non-criminal. Notable benefits of this study are increased reliability and accuracy in facial recognition system which is achieved through combining live picture recognition, still picture recognition and video image recognition. In addition, outstanding advantage of our solution is addressing the potential for bias in the algorithm by using diverse dataset that includes faces of different ages, gender, races, ethnicity and other characteristics. Furthermore, we explore the effectiveness of our solution with the respect to Dlib and Haar Cascade libraries and the results suggest that Haar Cascade library outperforms Dlib library in terms of execution time and accuracy.