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—This paper presents an efficient algorithm for face recognition using game theory. Texture based feature extraction techniques are popular for facial recognition, specifically those that segment a facial image into even sized regions, or patches. A cooperative game theory (CGT) based patch selector is exploited to select the most salient patches to extract features. The patches that have a stronger individual importance along with a strong interaction with other patches are selected. A modified local binary pattern (mLBP) feature extraction technique is utilized to extract features from each patch. The performance of the proposed scheme is validated using the Face Recognition Technology (FERET) database. Results show that compared to using mLBP alone, the CGT based selector outperforms it in regards to accuracy and amount of pathces used among different patch resolutions.

Facial Recognition Utilizing Patch Based Game Theory