On the Use of Discriminative Cohort Score Normalization for Unconstrained Face Recognition

Abstract

Abstract-Face recognition is one of the most widely used biometric systems due to its non-intrusive, natural and easy to use characteristics. However, automatic face recognition becomes very challenging whenever the acquisition conditions are unconstrained. In order to enhance the robustness of face recognition under challenging conditions, this paper proposes to adopt a cohort-based score normalization procedure. Specifically, the polynomial regression-based cohort normalization is extended to the unconstrained face pair matching problem. Extensive experiments conducted on the LFW benchmark demonstrate the effectiveness of cohort normalization on this challenging scenario. Furthermore, we advance the state of the art in cohort normalization by providing a better understanding of its discriminative cohort behavior. In particular, we find that the cohort information alone has a certain discriminative power which is just marginally worse than the raw matching score. A larger cohort set size gives more stable and often better results to a point before the performance saturates and slightly reduces. Finally, the experimental results on both the FRGC ver 2.0 database (lab face verification) and the LFW database (wild face pair matching) show that cohort samples of different quality indeed produce different cohort normalization performance. Generally, for matching faces captured under lab environments, using cohort samples of good quality leads to much better performance than using bad cohort samples. However, for matching wild faces, using wild cohort achieves the best performance.

Limits those are invisible to be disclosed by using new imageprocessing Techniques

“…The result pixel value will be the heavy sum of the insight pixels within the window where in fact the dumbbells will be the values of the filtration. The window with its loads is known as the kernel [3], [11]- [15].…”

Unconstrained Face Recognition Quality: A Review

“…This record primarily provided a better taking of discriminative legion efficiency joining. In distinction, with "wild", unchecked cohort examples permit reaching the perfect demonstration [25].…”

Unfamiliar Sides, Video, Image Enhancement in Face Recognition

“…This file mostly supplied an improved taking of discriminative legion performance joining. In variation, with "wild", uncontrolled cohort cases allow reaching the great exhibition [36].…”