Visual Observation Confidence based GMM Face Recognition robust to Illumination Impact in a Real-world Database

Abstract

The GMM is a conventional approach which has been recently applied in many face recognition studies. However, the question about how to deal with illumination changes while ensuring high performance is still a challenge, especially with real-world databases. In this paper, we propose a Visual Observation Confidence (VOC) measure for robust face recognition for illumination changes. Our VOC value is a combined confidence value of three measurements: Flatness Measure (FM), Centrality Measure (CM), and Illumination Normality Measure (IM). While FM measures the discrimination ability of one face, IM represents the degree of illumination impact on that face. In addition, we introduce CM as a centrality measure to help FM to reduce some of the errors from unnecessary areas such as the hair, neck or background. The VOC then accompanies the feature vectors in the EM process to estimate the optimal models by modified-GMM training. In the experiments, we introduce a real-world database, called KoFace, besides applying some public databases such as the Yale and the ORL database. The KoFace database is composed of 106 face subjects under diverse illumination effects including shadows and highlights. The results show that our proposed approach gives a higher Face Recognition Rate (FRR) than the GMM baseline for indoor and outdoor datasets in the real-world KoFace database (94% and 85%, respectively) and in ORL, Yale databases (97% and 100% respectively).

Energy-Efficient Biometrics-Based Remote User Authentication for Mobile Multimedia IoT Application

“…Image quality degradation affects face recognition accuracy [3][4][5]. These studies analyzed the effect of degradation due to image compression on face recognition accuracy.…”

“…The network's condition and compression rate affect transmission energy consumption. Therefore, the transmission energy consumption is described as equation (4). M is the compressed data size decided by the compression parameter (i.e., q), and b is the network bandwidth (i.e., unit: MB per second).…”

“…In traditional biometric recognition, the system consists of enrollment and identification; biometric information is stored remotely in a central database, and the identification device communicates with the database through a wired network [2]. Recently, some researchers have sought to integrate biometric recognition systems into wireless networks [3][4][5]. The biometric-based authentication systems are performed on the mobile devices with the battery, the improving energy efficiency is important issue.…”