Average-half-face in 2D and 3D using wavelets for face recognition
C. Gnanaprakasam, S. Sumathi, R. RaniHema Malini · 2010
Face recognition is one of the most hot and challengeable technologies, which is based on biometrics, and also one of the most potential technologies. As the most natural and friendly identification method, automatic face recognition has become the important part of the next generation computing technology. 3D face recognition methods are able to overcome the problems resulting from illumination, expression or pose variations in 2D face recognition. Utilize the symmetry of the face for face recognition, select average half-face for research. The average-half-face is constructed from the full frontal face image in two steps: First the face image is centered and divided in half and two halves are averaged together. The consequence of this discovery may result in substantial savings in storage and computation time. We would like to apply the average-half-face to facial feature extraction methods using wavelets. Applying the average-half-face to additional algorithms and databases (2D&3D) and analyzing the effect of illumination, facial expressions, occlusions and other difficulties would be helpful in identifying the most useful applications of the method. A face recognition method that is able to recognize faces at various angles is proposed. This method uses only the three dimensional range images for matching. PCA is one of the most successful techniques that have been in image recognition and compression. The purpose of PCA is to reduce the large dimensionality of the data space to the smaller intrinsic dimensionality of feature space, which is needed to describe the data economically.