Multi Modal Face Recognition Using Block Based Curvelet Features

Abstract

In this paper, we present multimodal 2D +3D face recognition method using block based curvelet features. The 3D surface of face (Depth Map) is computed from the stereo face images using stereo vision technique. The statistical measures such as mean, standard deviation, variance and entropy are extracted from each block of curvelet subband for both depth and intensity images independently.In order to compute the decision score, the KNN classifier is employed independently for both intensity and depth map. Further, computed decision scoresof intensity and depth map are combined at decision level to improve the face recognition rate. The combination of intensity and depth map is verified experimentally using benchmark face database. The experimental results show that the proposed multimodal method is better than individual modality.

A deep learning based stereo matching model for autonomous vehicle

“…Local methods rely on low level pixel features to compute the similarity in the cost computation step. They estimate the correspondence by means of a window or support region [13]- [15]. Since the pixel wise characterization play a major factor, a wide variety of these representations are used by researchers varying from a simple rgb representation of pixels to the other descriptors like census transform, scale invariant feature transform.…”