A New Framework for Matching Forensic Composite Sketches With Digital Images

Abstract

Face sketch recognition is considered as a sub-problem of face recognition. Matching composite sketches with its corresponding digital image is one of the challenging tasks. A new convolution neural network (CNN) framework for matching composite sketches with digital images is proposed in this work. The framework consists of a base CNN model that uses swish activation function in the hidden layers. Both composite sketches and digital images are trained separately in the network by providing matching pairs and mismatching pairs. The final output resulted from the network's final layer is compared with the threshold value, and then the pair is assigned to the same or different class. The proposed framework is evaluated on two datasets, and it exhibits an accuracy of 78.26% with extended-PRIP (E-PRIP) and 69.57% with composite sketches with age variations (CSA) respectively. Experimental analysis shows the improved results compared to state-of-the-art composite sketch matching systems.

An approach to partial occlusion using deep metric learning

“…The different types of occlusions occur in face is illustrated in Figure 1. The partial occluded features does not exist more in normal face recognition because the face is captured in a very good scenario with more lightning conditions [2]. But in forensic face recognition, face is captured in a worst scenario with poor lightning conditions.…”