Face Recognition and Gender Classification using Principal Component Analysis

Sarthi, Vijay Kumar (2014) Face Recognition and Gender Classification using Principal Component Analysis. MTech thesis.



Face recognition is one of the most challenging areas in the field of computer vision. In this thesis, a photometric (view based) approach is used for face recognition and gender classification. There exist several algorithms to extract features such as Principal Component Analysis (PCA), Fisher Linear Discriminate Analysis (FLDA), Image principal component analysis (IPCA), and various others. Principal component analysis is used for the dimensional reduction and for the feature extraction. Two face databases are taken in which one database contains the face images of male and one contains face images of females. On the basis of Euclidean Distance classification of the gender is done. Comparison between Euclidean Distance and Mahalanobis Distance for face recognition is also done with different number of test images. This method is tested on FERET and IIT KanpurŒs database.

Item Type:Thesis (MTech)
Uncontrolled Keywords:Face recognition, PCA, FERET database, Photometric, Euclidean Distance
Subjects:Engineering and Technology > Computer and Information Science > Image Processing
Divisions: Engineering and Technology > Department of Computer Science
ID Code:6482
Deposited By:Hemanta Biswal
Deposited On:12 Sep 2014 14:07
Last Modified:12 Sep 2014 14:07
Supervisor(s):Sa, P K

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