Face Recognition: A Novel Multi-Level Taxonomy based Survey

TL;DR

This paper introduces a comprehensive multi-level taxonomy for face recognition systems, enabling better organization, comparison, and development of solutions, supported by a detailed survey and discussion of future challenges.

Contribution

It proposes a new multi-level face recognition taxonomy that encompasses face structure, feature support, and extraction methods, aiding research and development.

Findings

A detailed taxonomy framework for face recognition systems

A comprehensive survey of existing face recognition solutions

Discussion of current challenges and future research directions

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.