Abstract—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.
Index Terms—Face recognition, modified local binary pattern (mlbp), game theory, and patch selection.
The authors are with the North Carolina A&T State University, Greensboro, NC 27411 USA (e-mail: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com).
Cite: Foysal Ahmad, Kaushik Roy, Brian O‟Connor, Joseph Shelton, Pablo Arias, Albert Esterline, and Gerry Dozier, "Facial Recognition Utilizing Patch Based Game Theory," International Journal of Machine Learning and Computing vol. 5, no. 4, pp. 334-338, 2015.