2D-3D Mixed Face Recognition Schemes

Automatic recognition of people is a challenging problem which has received much attention during the recent years [FRHomepage, AFGR, AVBPA] due to its potential applications in different fields such as law enforcement, security applications or video indexing. Face recognition is a very challenging problem and up to date, there is no technique that provides a robust solution to all situations and different applications that face recognition may encounter. Most of the face recognition techniques have evolved in order to overcome two main challenges: illumination and pose variation [ FRVT02, FRGC05, Zhao03, Zhao06]. Either of these problems can cause serious performance degradation in a face recognition system. Illumination can change the appearance of an object drastically, and in the most of the cases these differences induced by illumination are larger than differences between individuals, what makes difficult the recognition task. The same statement is valid for pose variation. Usually, the training data used by face recognition systems are frontal view face images of individuals [Brunelli93, Nefian96, Turk91, Pentland94, Lorente99, Belhumeur97, Bartlett02, Moghaddam02, Delac05, Kim02, Schölkopf98, Schölkopf99, Yang02, Yang04, , Wang06, Yu06, Heo06]. Frontal view images contain more specific information of a face than profile or other pose angle images. The problem appears when the system has to recognize a rotated face using this frontal view training data. Furthermore, the appearance of a face can also change drastically if the illumination conditions vary [Moses94]. Therefore, pose and illumination (among other challenges) are the main causes for the degradation of 2D face recognition algorithms. Some of the new face recognition strategies tend to overcome both challenges from a 3D perspective. The 3D data points corresponding to the surface of the face may be acquired using different alternatives: a multi camera system (stereoscopy) [Onofrio04, Pedersini99, structured light [Scharstein02, 3DRMA], range cameras or 3D laser and scanner devices [Blanz03, Bowyer04, Bronstein05]. The main advantage of using 3D data is that depth information does not depend on pose and illumination and therefore the representation of the object do not change with these parameters, making the whole system more robust. O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg