A Combination of Cross Domain Features for Face Recognition

Face recognition in biometrics is a difficult process because of factors including individual differences in facial emotions, illumination, resolution, facial rotation, and facial structure similarities. "A Combination of Cross Domain Features for Face Recognition" is what we suggest here. The benchmarked face images are considered, downsized to consistent 128x128 dimensions, and converted to grayscale. Face photos are compressed, and their quality is improved by using the Discrete Wavelet Transform (DWT). Initial characteristics that are unaffected by changes in an image's lighting are extracted using the Histogram of Oriented Gradients (HOG) on a compressed LL band of DWT. By connecting the LL band's output to the HOG's input, the DWT and HOG's LL bands are connected in a cascade. HOG's input and output are combined to create new, strong final features. The system's results when comparing test and database features are calculated using the Euclidean Distance (ED). It has been noted that the suggested system outperforms the current ones in terms of results.