Face Recognition in Unconstrained Images Using Deep Learning Model for Forensics

ABSTRACT Partially obscuring the face with a hat, sunglasses, scarf, or beard is referred to as partial occlusions. They are very common in forensic face recognition applications because facial images are captured in unconstrained environments. To address the issue of partial occlusion, two significant spatial domain approaches using deep learning techniques are proposed in this research work. The first approach exploits deep metric‐based learning (DML) and spatial features for face recognition. Convolution‐based face finding (CFF) technique using CNN for digital forensic face recognition tasks is employed in the second approach. Proposed approaches are experimented on the Disguised Faces in Wild (DFW) dataset, and a comparative analysis of the proposed approaches with existing approaches is presented. It is observed that the CFF technique using CNN provides a recognition accuracy of 93.75%, which performs better than DML and the existing approaches. The research work presented has significance, and results are promising in the field of unconstrained face recognition.