Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis | Zendy

Leila Boussaad | Zendy; Mohamed Benmohammed | Zendy; Redha Benzid | Zendy

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Age Invariant Face Recognition Based on DCT Feature Extraction and Kernel Fisher Analysis

Author(s) -

Leila Boussaad,

Mohamed Benmohammed,

Redha Benzid

Publication year - 2016

Publication title -

journal of information processing systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.288

H-Index - 23

eISSN - 2092-805X

pISSN - 1976-913X

DOI - 10.3745/jips.02.0043

Subject(s) - computer science , invariant (physics) , pattern recognition (psychology) , artificial intelligence , discrete cosine transform , feature extraction , kernel (algebra) , facial recognition system , fisher kernel , kernel fisher discriminant analysis , mathematics , image (mathematics) , discrete mathematics , mathematical physics

The aim of this paper is to examine the effectiveness of combining three popular tools used in pattern recognition, which are the Active Appearance Model (AAM), the two-dimensional discrete cosine transform (2D-DCT), and Kernel Fisher Analysis (KFA), for face recognition across age variations. For this purpose, we first used AAM to generate an AAM-based face representation; then, we applied 2D-DCT to get the descriptor of the image; and finally, we used a multiclass KFA for dimension reduction. Classification was made through a K-nearest neighbor classifier, based on Euclidean distance. Our experimental results on face images, which were obtained from the publicly available FG-NET face database, showed that the proposed descriptor worked satisfactorily for both face identification and verification across age progression.

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