Face recognition: a novel multi‐level taxonomy based survey

Abstract

In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. To help understanding the landscape and abstraction levels relevant for face recognition systems, face recognition taxonomies allow a deeper dissection and comparison of the existing solutions. This paper proposes a new, more encompassing and richer multi-level face recognition taxonomy, facilitating the organization and categorization of available and emerging face recognition solutions; this taxonomy may also guide researchers in the development of more efficient face recognition solutions. The proposed multi-level taxonomy considers levels related to the face structure, feature support and feature extraction approach. Following the proposed taxonomy, a comprehensive survey of representative face recognition solutions is presented. The paper concludes with a discussion on current algorithmic and application related challenges which may define future research directions for face recognition.

An End-to-End Deep Model With Discriminative Facial Features for Facial Expression Recognition

“…There are many typical applications of deep learning in face recognition, including face recognition method based on CNNs, deep non-linear face shape extraction method, robust modeling of face pose based on deep learning, fully automatic face recognition in a constrained environment, face recognition under video surveillance based on deep learning, low-resolution face recognition based on deep learning, and other face-related information recognition based on deep learning [18], [37]- [39]. Among them, CNN is the better learning algorithm that successfully trains a multilayer network structure.…”

Analysis of RGB-D camera technologies for supporting different facial usage scenarios

“…In this article, Face Identification refers to that variety of applications performing Face Recognition [88] without authentication purposes described in the previous section. Some examples can be found in the fields of security, for criminals identification [79], marketing, to target specific customers or at least some of their features such as age and gender [17], and healthcare, for a health monitoring through a comparison between the current status of a patient and an image of the same patient in good health [49].…”

Age-based facial recognition using convoluted neural network deep learning algorithm

“…Stephen [5] given a brief review of the techniques of deep learning and face-to-face learning and compares some of the basic neural formulas based on common convolution neural networks (CNNs). The deep networks used in FR, such as deep belief network (DBN), convolutional neural network (CNN, or ConvNet), autoencoder (AE), and others are analyzed for architecture [6]. Mandal [7] assessed a significant measure of profound learning strategies for FR.…”

“…Mandal [7] assessed a significant measure of profound learning strategies for FR. Sepas-Moghaddam [6] studied of FR arrangements dependent on another, all the more incorporating and more extravagant staggered scientific classification. Learned-Miller [8] looked at a variety of surprising inventive strategies in the Labeled Faces in the Wild (LFW) database.…”