Face Recognition Using Gabor Wavelet for Image Processing Applications

The selection of appropriate wavelets is an important target for any application. In this paper Face recognition has been performed using Principal component analysis (PCA), Gaussian based PCA and Gabor based PCA. PCA extracts the relevant information from complex data sets and provides a solution to reduce dimensionality. PCA is based on Euclidean distance calculation which is minimized by applying Gabor filter as compared to Gaussian Filter to enhance the accuracy for Face recognition. The experiments shows that the proposed method (PCA) can effectively reduced the computational complexity. Gabor based PCA shows 99.74% more accurate result as compare to 94.54% Gaussian based PCA for face recognition.