3D face recognition: A robust multi-matcher approach to data degradations

Abstract

Over the past decades, 3D face has emerged as a solution to face recognition due to its reputed invariance to lighting conditions and pose. While proposed approaches have proven their efficiency over renowned databases as FRGC, less effort was spent on studying the robustness of algorithms to quality degradations. In this paper, we present a study of the robustness of four state of the art algorithms and a multi-matcher framework to face model degradations such as Gaussian noise, decimation, and holes. The four state of the art algorithms were chosen for their different and complementary properties and exemplify the major classes of 3D face recognition solutions. As they displayed different behavior under data degradations, we further designed a fusion framework to best take into account their complementary properties. The proposed multi-matcher scheme is based on an offline and an online weight learning process. Experiments were conducted on a subset of the FRGC database, on which we generated degradations. Results demonstrate the competitive robustness of the proposed approach.

Automatic Analysis of Smoothing Techniques by Simulation Model Based Real-Time System for Processing 3D Human Faces

“…In this literature study, its importance in face registration and (or) recognition has particularly an impact for developing an array of smoothing techniques implementing in real time system and illustrating their significance for processing purpose. Reference Description [7] Authors have demonstrated the effect of the median filter for removing sharp spikes, and again interpolation technique has been added to fill the holes on the face image. [8] Authors have compared the performance of landmark localization technique with array of smoothing methods, namely Max Filter, Min filter, Gaussian filter, Mean filter, and Weighted median filter.…”