Temporal Synchronization and Normalization of Speech Videos for Face Recognition

Automatic Face Recognition (AFR) is a domain that provides various advantages over other biometrics, such as acceptability and ease of use, but due to the current trends, the identification rates are still low as compared to more traditional biometrics, such as fingerprints. Image based face recognition, was the mainstay of AFR for several decades but quickly gave way to video based AFR with the arrival of inexpensive video cameras and enhanced processing power. Video based face recognition has several advantages over image based techniques, the two main being, more data for pixel-based techniques, and availability of temporal information. But with these advantages there are some inconveniences also, the foremost being the augmentation of variation. In the classical image based face recognition degraded performance has mostly been attributed to three main sources of variation in the human face, these being pose, illumination and expression. Among these, pose has been quite problematic both in its effects on the recognition results and the difficulty to compensate for it. Techniques that have been studied for handling pose in face recognition can be classified in 3 categories, first are the ones that estimates an explicit 3D model of the face (Blanz & Vetter, 2003) and then use the parameters of the model for pose compensation, second are subspace based such as eigenspace (Matta & Dugelay, 2008) and the third type are those which build separate subspaces for each pose of the face such as view-based eigenspace (Lee & Kriegman, 2005). Managing illumination variation in videos has been relatively less studied as compared to pose, mostly image based techniques are extended to video. The two classical image based techniques that have been extended for video with relative success are illumination cones (Georghiades et al., 1998) and 3D morphable models (Blanz & Vetter, 2003). Lastly expression invariant face recognition techniques can be divided in two categories, first are based on subspace methods that model the facial deformations (Tsai et al., 2007). Next are techniques that use morphing techniques (Ramachandran et al., 2005), who morph a smiling into a neutral face.