Face recognition is an important pattern recognition problem in the study of natural and artificial learning systems. In typical optical image based face recognition systems, the systematic variability that arises from representing the three dimensional (3D) shape of a face by a two dimensional (2D) illumination intensity matrix is treated as a random variable, and it is obtained by collecting examples of faces in different poses with respect to the camera. More sophisticated 3D recognition systems employ specialist equipment (e.g. laser scanners) to measure the shape of the face, and they perform either pattern matching in three dimensions or they use projections from 3D models to match against 2D images. It is shown here that optical images obtained with a pair of stereo cameras may be used to extract depth information in the form of disparity values, and thereby significantly enhance the performance of a face recognition system.
['Anjali Bharatkumar Samani', 'Joab R. Winkler', 'Mahesan Niranjan']
Automatic Face Recognition Using Stereo Images