Abstract

Face verification performance by human brain has been shown to be much better than most of the state-of-the-art approaches in computer vision. Performance improvement of automated face recognition (FR) systems that may equal or surpass the human intellect is the key goal of this research. We present our face verification system using facial mark (FM) combined with deep convolutional neural network (DCNN) approach to improve the overall FR accuracy. We propose to use FM (e.g., scars, moles and freckles) for face matching in the wild where the FM detection is performed on mean faces as well as affine aligned normalized facial images. The FR experiments are carried out on IARPA Janus Benchmark-A (IJB-A) dataset which includes real-world unconstrained images from 500 subjects. The IJB-A datasets includes full pose, expression, and illumination variations which are much harder than traditional …

Date

2020

Authors

Sidra Riaz, Unsang Park, Prem Natarajan

Journal

Image and Vision Computing

Volume

104

Pages

104020

Publisher

Elsevier