A Review of Face Recognition Methods

Abstract

Face recognition has become more significant and relevant in recent years owing to it potential applications. Since the faces are highly dynamic and pose more issues and challenges to solve, researchers in the domain of pattern recognition, computer vision and artificial intelligence have proposed many solutions to reduce such difficulties so as to improve the robustness and recognition accuracy. As many approaches have been proposed, efforts are also put in to provide an extensive survey of the methods developed over the years. The objective of this paper is to provide a survey of face recognition papers that appeared in the literature over the past decade under all severe conditions that were not discussed in the previous survey and to categorize them into meaningful approaches, viz. appearance based, feature based and soft computing based. A comparative study of merits and demerits of these approaches have been presented.

A feature based comparison of pen and swipe based signature characteristics

“…The physiological biometrics relies on the physical attributes of an individual such as fingerprints, facial features or even DNA. Research in the area of biometrics technologies highlight advancements to better facilitate these modalities into their associated applications (Beham & Roomi, 2013;Koon, Yang, & Tseng, 2014;Lakshmanan, Selvaperumal, & Mun, 2014). Physiological modalities are commonly implemented into large-scale biometric deployments such as Automated Border Control (ABC) systems (Nuppery, 2014) and access control (Maghiros, Puine, Delaitre, & Lignos, 2005).…”

An interdisciplinary taxonomy of social cues and signals in the service of engineering robotic social intelligence

“…Although many facial recognition systems now perform at satisfactory levels in controlled environments, there are still limitations to this technology, and progress in the field has become constrained due to many factors [45][46][47] . Human faces are visually salient objects within a scene, and researchers replicating the human model of socially-salient perception in artificial social agents [48][49][50] .…”

Recognition of Faces using Efficient Multiscale Local Binary Pattern and Kernel Discriminant Analysis in Varying Environment

“…Face recognition is done by various researchers as reported by Beham and Roomi (2013) using various methods and techniques. Features are extracted by combining PCA and LDP is proposed by Luo et al (2015).…”