Journal
IET IMAGE PROCESSING
Volume 12, Issue 10, Pages 1807-1814Publisher
WILEY
DOI: 10.1049/iet-ipr.2017.1263
Keywords
face recognition; image representation; image classification; iterative methods; Gabor filters; image filtering; feature extraction; minimisation; fast matching pursuit; sparse representation-based face recognition; pattern recognition problems; Gabor feature extraction; supervised locality-preserving projections; SLPP; heat kernel weights; sparse representation-based classification; SRC; (1) minimisation; benchmark face databases; recognition rate
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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 minimisation, the authors propose performing SRC using fast matching pursuit, which considerably improves the classification performance. The authors' proposed framework achieves approximate to 99% recognition rate using four benchmark face databases, significantly faster than related frameworks.
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