Access control based on gait analysis and face recognition

Abstract

According to a new market research report, electronic access control system is expected to be worth $16.3 billion by 2017. Vision-based biometric authentication systems have received much attention with increasing demands for long distance surveillance applications and access control to the security area. Such visual application is mainly focused on face recognition. Nevertheless, small size or poor quality images with varying poses, illumination, expressions, glasses or hats and so on can perturb the recognition. To deal with such problems, the fusing of both face recognition and gait analysis is proposed. Gait has recently become one of the most emerging biometrics for non-intrusive person identification. A system is proposed in which information provided by face recognition and gait analysis is fused to identify people. Such a system has direct industrial applications since it is a non-intrusive security system that is reliable, easily deployable and just needs an inexpensive camera.

Radar-Based Non-Contact Continuous Identity Authentication

“…Compared to these contact-based solutions, continuous authentication using non-contact, unobtrusive techniques, such as Doppler radar, can further improve system usability and expand the range of applications into domains with known privacy concerns [43,44]. For example, various visible and thermal-based cameras are employed to acquire face and gait features for user verification [45][46][47]. However, image-based approaches suffer from several irreconcilable dilemmas, including a lack of privacy and degraded performance under a low light ambient conditions [48,49].…”

Hierarchical extended collaborative representation based classification for single‐sample face recognition

“…Face recognition (FR) is one of the most crucial tasks in computer vision and pattern recognition, and is widely used in practical applications, such as access control, e-passport, and criminal recognition [1][2][3]. Many classical algorithms have been proposed to address this issue, such as nearest neighbour classification (NN) [4], linear support vector machine (SVM) [5], and linear regression classification (LRC) [6].…”

Lightweight Biometric Sensing for Walker Classification Using Narrowband RF Links

“…From the perspective of the degree of cooperation of the subjects in the phase of feature acquisition, the existing systems can be categorized into constrained and unconstrained manners [ 9 , 10 ]. In the constrained sensing-based systems, the capture of physiological traits such as fingerprint [ 11 ], palm print [ 12 ], and iris [ 13 ] depends on high-quality and short-range sensors.…”

“…In unconstrained biometric capture, the physiological and behavioral traits are collected far away from the sensor. Various visible and thermal camera-based vision techniques have been applied to the acquisition of face and gait features [ 9 ]. The rapid developments of face identification systems are capable of recognizing and verifying personal identity in a passive and non-intrusive manner [ 17 ].…”