Comparison of face matching techniques under pose variation
Proceedings of the 6th ACM international conference on Image and video retrieval
Eye localization for face matching: is it always useful and under what conditions?
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Face recognition from still images to video sequences: a local-feature-based framework
Journal on Image and Video Processing - Special issue on advanced video-based surveillance
| Hi-index | 0.00 |
In a real-world environment a face detector can be applied to extract multiple face images from multiple video streams without constraints on pose and illumination. The extracted face images will have varying image quality and resolution. Moreover, also the detected faces will not be precisely aligned. This paper presents a new approach to on-line face identification from multiple still images obtained under such unconstrained conditions. Our method learns a sparse representation of the most discriminative descriptors of the detected face images according to their classification accuracies. On-line face recognition is supported using a single descriptor of a face image as a query. We apply our method to our newly introduced BHG descriptor, the SIFT descriptor, and the LBP descriptor, which obtain limited robustness against illumination, pose and alignment errors. Our experimental results using a video face database of pairs of unconstrained low resolution video clips of ten subjects, show that our method achieves a recognition rate of 94% with a sparse representation containing 10% of all available data, at a false acceptance rate of 4%.