This thesis proposes a face recognition method under varying pose, facial expression and lighting conditions. Binary thresholding techniques were used to identify important facial regions before fitting of ellipsoid to extract facial boundary. Using relative positional information of eyes and nose, the nose region is assumed and the nose top is localized and defined to be the center of the face. Lattice features between the face center and the boundary of the face are then computed. Face recognition is achieved by lattice matching. For the experiment, the ORL face database is used. Our experiments showed that 98% recognition rate could be achieved with the proposed face recognition model with precise feature extractions (human interactions). However, given our method for facial center localization and facial boundary detection, recognition rate of 93% can still be achieved.