Skip to Main Content
This paper compares the performance of face recognition systems based on principal component analysis (PCA), Gabor wavelets (GW) and discrete wavelet transform (DWT). The three techniques are implemented in the MATLAB programming environment, and their performance is investigated using frontal facial images from the FERET database. The images are preprocessed to yield a standardized image used for identification. PCA produces an orthonormal basis for the image space that extracts the dominant facial features, providing exceptional recognition performance. The GW technique is modelled after biological experiments and is used to filter spatial-frequency features of the image at key points of the face. The DWT is investigated for its potential use in facial-feature extraction and is also applied to rotated versions of the facial image, thereby increasing the directional filtering capability. A face similarity measure that uses the extracted features provides recognition that is robust against variations in illumination.