2d-3d Pose Invariant Face Recognition System for Multimedia Applications

Automatic Face recognition of people is a challenging problem which has received much attention during the recent years due to its potential multimedia applications in different fields such as 3D videoconference, security applications or video indexing. However, there is no technique that provides a robust solution to all situations and different applications, yet. Face recognition includes a set of challenges like expression variations, occlusions of facial parts, similar identities, resolution of the acquired images, aging of the subjects and many others. Among all these challenges, most of the face recognition techniques have evolved in order to overcome two main problems: illumination and pose variation. Either of these influences can cause serious performance degradation in a 2D face recognition system.Some of the new face recognition strategies tend to overcome both research topics 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), structured light, range cameras or 3D laser and scanner devices The main advantage of using 3D data is that geometry information does not depend on pose and illumination and therefore the representation of the object does not change with these parameters, making the whole system more robust. Howev-2 er, the main drawback of the majority of 3D face recognition approaches is that they need all the elements of the system to be well calibrated and synchronized to acquire accurate 3D data (texture and depth maps). Moreover, most of them also require the cooperation or collaboration of the subject during a certain period of time. All these requirements can be available during the training stage of many applications. When enrolling a new person in the database, it could be performed off-line, with the help o human interaction and with the cooperation of the subject to be enrolled. On the contrary, the previous conditions are not always available during the test stage. The recognition will be in most of the cases in a semi-controlled or uncontrolled scenario, where the only input of the system will probably consists of a 2D intensity image acquired from a single camera. This leads to a new paradigm where 2D-3D mixed face recognition approaches are used. The idea behind this kind of approaches is that these take profit of the 3D data during the training stage but then they can use either 3D data (when available) or 2D data during …