Enhanced Face Recognition through Variation of Principal Component Analysis (PCA)

by   Dulani Meedeniya, et al.

Face recognition, the art of matching a given face to a database of faces, is a non-intrusive biometric method that dates back to the 1960s. Facial recognition systems are built on computer programs that analyze images of human faces for the purpose of identifying them. The paper presents a new method for face recognition. This can cope with different lightning conditions and different distorted levels in facial images. This method relies on a variation of Principal Component Analysis (PCA) technique. The algorithm extracts the eigan values and eigan vectors from the images. It performs the economic size singular value decomposition to obtain a unitary matrix, which is use for recognition. The images are recognized base on the minimum distance. The system finds the closest match from the database to the incoming image. The system uses the “Olivetti face database” as the face image database. The database contains 10 images of each person in the group. The proposed system will take a picture that is not included in the database and match it to a picture of the same person that is within the image database. Experimental results demonstrate that the proposed approach can efficiently recognize human faces. This system satisfactorily deals with the problems caused by using other face recognition systems. This algorithm can achieve 93.7% and higher performance. Successful results were obtained in different situations where images have taken under different lighting conditions. The proposed method reduces the computational load. In comparison with the traditional use of PCA, the proposed method gives better recognition accuracy and discriminatory power.


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