Open AccessFast matching pursuit for sparse representation‐based face recognition
Author(s) -
Melek Michael,
Khattab Ahmed,
AbuElyazeed Mohamed F.
Publication year - 2018
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2017.1263
Subject(s) - facial recognition system , pattern recognition (psychology) , artificial intelligence , computer science , sparse approximation , feature extraction , matching pursuit , locality , kernel (algebra) , three dimensional face recognition , face detection , mathematics , compressed sensing , linguistics , philosophy , combinatorics
Even though face recognition is one of the most studied pattern recognition problems, most existing solutions still lack efficiency and high speed. Here, the authors present a new framework for face recognition which is efficient, fast, and robust against variations of illumination, expression, and pose. For feature extraction, the authors propose extracting Gabor features in order to be resilient to variations in illumination, facial expressions, and pose. In contrast to the related literature, the authors then apply supervised locality‐preserving projections (SLPP) with heat kernel weights. The authors’ feature extraction approach achieves a higher recognition rate compared to both traditional unsupervised LPP and SLPP with constant weights. For classification, the authors use the recently proposed sparse representation‐based classification (SRC). However, instead of performing SRC using the computationally expensive ℓ 1 minimisation, the authors propose performing SRC using fast matching pursuit, which considerably improves the classification performance. The authors’ proposed framework achieves ∼99% recognition rate using four benchmark face databases, significantly faster than related frameworks.