Multi Modal Face Recognition Using Block Based Curvelet Features

TL;DR

This paper introduces a multimodal face recognition approach combining 2D intensity and 3D depth data using block-based curvelet features, which enhances recognition accuracy over single-modality methods.

Contribution

It proposes a novel multimodal face recognition method utilizing block-based curvelet features and decision-level fusion of intensity and depth data.

Findings

Multimodal approach outperforms individual modality recognition.

Decision-level fusion improves recognition accuracy.

Experimental validation on benchmark database confirms effectiveness.

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.