Real-time facial feature extraction using statistical shape model and Haar-wavelet based feature search

We propose a fast facial feature extraction technique for an embedded face recognition system. The novel key element is a combination of a statistical shape model and the application of a Haar-wavelet based feature matching. Our statistical face model is based on the active shape model (ASM). However ASM lacks robustness to illumination changes and it has a limited convergence area. Instead of a 1D profile analysis, we propose a 2D texture pattern search-and-fitting scheme, which provides more robustness and faster convergence than conventional ASM. Furthermore, we employ Haar-wavelets to model local-facial textures, which yields two improvements: faster processing and more robustness with respect to low-quality images. Our proposed approach shows good results dealing with test face images, which are quite dissimilar with the faces used for statistical training. The convergence area of our proposed method almost quadruples compared to ASM, and the extraction accuracy is also improved. The total processing requires 30 - 70 ms, which is comparable to ASM, but faster than the active appearance model (AAM).