Real-Time Face Recognition Using Local Ternary Patterns with Collaborative Representation-Based Classification for Mobile Robots

The ability to recognize faces is a crucial element for human-robot interaction. In this paper, we present an algorithm for mobile robots to detect, track, and recognize human faces accurately, even when humans go through different illumination conditions. We track faces using a tracker that combines the algorithm of an adaptive correlation filter with a Viola-Jones object detection. This tracker adapts to scale changes and to rotation of the face, and its occlusion. It also adapts to complex changes of background and illumination. Recognizing the tracked face is established by using an algorithm that combines local ternary patterns and collaborative representation based classification (CRC). This combination enhances the efficiency of face recognition under different illumination and noisy conditions. Our method achieves high recognition rates on challenging face databases and can run in real time on mobile robots.