Robust real time face recognition and tracking on gpu using fusion of rgb and depth image

TL;DR

This paper introduces a real-time face recognition and tracking system that fuses RGB and depth images using GPU acceleration, achieving robustness and speed improvements over traditional methods.

Contribution

The novel fusion of depth and RGB data on GPU for robust, high-speed face recognition and tracking using a modified LBP feature and SVM classifier.

Findings

Significant speed improvements with GPU implementation.

Enhanced robustness using depth information.

Effective real-time multi-face recognition.

Abstract

This paper presents a real-time face recognition system using kinect sensor. The algorithm is implemented on GPU using opencl and significant speed improvements are observed. We use kinect depth image to increase the robustness and reduce computational cost of conventional LBP based face recognition. The main objective of this paper was to perform robust, high speed fusion based face recognition and tracking. The algorithm is mainly composed of three steps. First step is to detect all faces in the video using viola jones algorithm. The second step is online database generation using a tracking window on the face. A modified LBP feature vector is calculated using fusion information from depth and greyscale image on GPU. This feature vector is used to train a svm classifier. Third step involves recognition of multiple faces based on our modified feature vector.