Facial Expression Recognition: Fusion of A Human Vision System Model and A statistical framework

Automatic facial expression recognition from still face (color and gray-level) images is acknowledged to be complex in view of significant variations in the physiognomy of faces with respect to head pose, environment illumination and person-identity. Even assuming illumination and pose invariance in face images, recognition of facial expressions from novel persons always remains an interesting and also challenging problem. With the goal of achieving significantly improved performance in expression recognition, the proposed new algorithms, combining bio-inspired approaches and statistical approaches, involve (a) the extraction of contour-based features and their radial encoding; (b) a modification of HMAX model using local methods; and (c) a fusion of local methods with an efficient encoding of Gabor filter outputs and a combination of classifiers based on PCA and FLD. In addition, the sensitivity of existing expression recognition algorithms to facial identity and its variations is overcome by a novel composite orthonormal basis that separates expression from identity information. Finally, by way of bringing theory closer to practice, the proposed facial expression recognition algorithm has been efficiently implemented for a web application.

Author dc:creator

• GU WENFEI

• Facial Expression Recognition, Human Vision System, Local Gabor Features, Radial Encoding, Classifier Synthesis, Composite Orthonormal Basis