Enhancing Low Quality Face Image Matching by Neurovisually Inspired Deep Learning

摘要

Computerized human face matching from low quality images is an active area of research in deformable pattern recognition especially in non-cooperative security, surveillance, authentication and multi-camera tracking. In low resolution and motion-blurry face images captured from surveillance cameras, it is challenging to get good match of faces and even extracting suitable feature vectors both in classical signal/image processing based and deep learning based approaches. In the current work, we have proposed a novel low quality face image matching algorithm in the light of a neuro-visually inspired method of figure-ground segregation (NFGS). The said framework is inspired by the nonlinear interaction between the classical receptive field (CRF) and its non-classical extended surround, comprising of the non-linear mean increasing and decreasing sub-units. The current work demonstrates not only better detection of low quality face images in NFGS enabled deep learning framework, but also it. prescribes an efficient way of low quality face image matching addressing low contrast, low resolution and motion blur which are prime responsible factors of making image low quality. The experimental results shows the effectiveness of proposed algorithm not only quantitatively but also qualitatively in terms of psycho-visual experiments and its statistical analysis outcome.