Title :
Robust non-rigid 3D tracking for face recognition in real-world videos
Author :
Zhang, Wei ; Qiao, Yu ; Xu, Chunjing ; Chen, Shifeng
Author_Institution :
Shenzhen Institutes of Adv. Technol., CAS, Shenzhen, China
Abstract :
In this paper, we propose a framework for face recognition in real-world noisy videos. The difficulty of video face recognition task lies in the challenging appearance variations in real-world videos due to motion blur, large head rotation, occlusion, illumination change and significant image noise. We utilize a non-rigid face tracking approach to address these problems, which makes good use of 3D face shape priors, local appearance model of major facial features, face silhouette and online feature matches across video frames. The benefits are twofold. Firstly, the 3D tracking algorithm can achieve accurate registration of faces in videos. Compared with the state-of-the-art approaches which rely on discriminative appearance models to classify face images into different views, we directly estimate face pose for a view-based face recognition algorithm. Secondly, since the 3D tracking algorithm has a probabilistic form and can provide confidence measure on the tracking result, it can be used to improve the robustness of face recognition. With the precisely localized faces, the recognition process is performed with different feature descriptors. The experiments performed on the real world noisy videos from YouTube demonstrate a significant improvement achieved even with the usage of simple descriptors: the rank-1 recognition rate reaches 79.8%, while the best reported results from other works is 71.24% on the same dataset.
Keywords :
face recognition; image classification; image matching; image motion analysis; image registration; image restoration; pose estimation; solid modelling; video signal processing; 3D face shape priors; discriminative appearance model; face image classification; face pose; face registration; face silhouette; facial feature; image noise; large head rotation; motion blur; online feature matching; probabilistic form; rank-1 recognition rate; real-world noisy video frame; robust nonrigid 3D face tracking algorithm; video face recognition task; Deformable models; Face; Face recognition; Shape; Solid modeling; Three dimensional displays; Videos; Non-Rigid 3D Tracking; Video Based Recognition;
Conference_Titel :
Information and Automation (ICIA), 2011 IEEE International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4577-0268-6
Electronic_ISBN :
978-1-4577-0269-3
DOI :
10.1109/ICINFA.2011.5949124