2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA
This is an incremental improvement for face recognition systems, offering better efficiency and performance compared to using full Gabor filter banks.
The paper tackled face recognition by using a subset of Gabor filters and LDA for feature extraction, achieving an average recognition rate of 98.9% on standard databases.
We present a new approach for face recognition system. The method is based on 2D face image features using subset of non-correlated and Orthogonal Gabor Filters instead of using the whole Gabor Filter Bank, then compressing the output feature vector using Linear Discriminant Analysis (LDA). The face image has been enhanced using multi stage image processing technique to normalize it and compensate for illumination variation. Experimental results show that the proposed system is effective for both dimension reduction and good recognition performance when compared to the complete Gabor filter bank. The system has been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and achieved average recognition rate of 98.9 %.