Face Recognition: A Novel Multi-Level Taxonomy based Survey
This work provides a structured framework to help researchers and practitioners understand and compare face recognition systems, addressing the need for better organization in a field with broad applications in security and other areas.
The paper proposes a new multi-level taxonomy for organizing and categorizing face recognition solutions, based on face structure, feature support, and extraction approach, and includes a comprehensive survey of existing methods.
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.