A frequency domain face recognition technique based on correlation plane features as input to a regression neural network | Zendy

Pradipta K. Banerjee | Zendy; Jayanta K. Cahndra | Zendy; Asit Datta | Zendy

Open Access

A frequency domain face recognition technique based on correlation plane features as input to a regression neural network

Author(s) -

Pradipta K. Banerjee,

Jayanta K. Cahndra,

Asit Datta

Publication year - 2010

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2010.11.010

Subject(s) - thresholding , computer science , pattern recognition (psychology) , artificial intelligence , feature (linguistics) , artificial neural network , facial recognition system , face (sociological concept) , correlation , plane (geometry) , frequency domain , feature vector , speech recognition , mathematics , computer vision , image (mathematics) , social science , philosophy , linguistics , geometry , sociology

In the area of Face Recognition (FR), the frequency domain approach is a widely used technique. The value of peak-to-sidelobe ratio (PSR) of the correlation peak at the output plane is often used as a measure of recognition. However, the use of such a hard threshold can make a FR system erroneous for recognition and classification. As an alternative to the conventional hard thresholding, three different feature vectors of the correlation plane, e.g. the peak intensity, total energy and PSR, are calculated. These three feature vectors are used to train a Generalized Regression Neural Network (GRNN). The improved False Acceptance Rate (FAR) and consistent Recognition Rate (RR) is reported when tested on the Yale face database

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