Abstract
In recent years, due to the fantastic applications revealed in the mass media are progressively walking out to the reality, face recognition is getting more and more attention to people all over the world. Between the two modes of face recognition, verification is simpler and more suitable than identification in some practical applications such as authentication. To describe the information in human faces more elaborately, we prefer the local appearance-based methods among the various face recognition approaches. In this thesis, we studied three local appearance-based methods: GOP-Face (Gradient Orientation Pyramid), LBP-Face (Local Binary Pattern) and DT-CWT-Face (Dual Tree-Complex Wavelet Transform), and tried to give a clear overview of these three methods. Furthermore, we use face verification to experiment their robustness to variations like spatial shift, illumination changes and age progression on the ORL, Yale and FERET databases with k nearest neighbor classifier. The results verified that LBP-Face and DT-CWT-Face are actually more robust to the spatial shift and DT-CWT-Face is surprisingly robust to age progression, and is even better than GOP-Face. However, the performance against illumination change is not as good as expected.