Robust speaking face identification for video analysis

Authors:
Yi Wu;Wei Hu;Tao Wang;Yimin Zhang;Jian Cheng;Hanqing Lu
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science and Intel China Research Center, Beijing, P.R. China;Intel China Research Center, Beijing, P.R. China;Intel China Research Center, Beijing, P.R. China;Intel China Research Center, Beijing, P.R. China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science
Venue:
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Year:
2007

Citing 6
Cited 2

Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Automatic Face Recognition for Film Character Retrieval in Feature-Length Films

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Identifying Individuals in Video by Combining "Generative" and Discriminative Head Models

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Visual Speech Recognition with Loosely Synchronized Feature Streams

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Robust head tracking with particles based on multiple cues fusion

ECCV'06 Proceedings of the 2006 international conference on Computer Vision in Human-Computer Interaction

Naming faces in films using hypergraph matching

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Character identification in feature-length films using global face-name matching

IEEE Transactions on Multimedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

We investigate the problem of automatically identifying speaking faces for video analysis using only the visual information. Intuitively, mouth should be first accurately located in each face, but this is extremely challenging due to the complicated condition in video, such as irregular lighting, changing face poses and low resolution etc. Even though we get the accurate mouth location, it's still very hard to align corresponding mouths. However, we demonstrate that high precision can be achieved by aligning mouths through face matching, which needs no accurate mouth location. The principal novelties that we introduce are: (i) proposing a framework for speaking face identification for video analysis; (ii) detecting the change of the aligned mouth through face matching; (iii) introducing a novel descriptor to describe the change of the mouth. Experimental results on videos demonstrated that the proposed approach is efficient and robust for speaking face identification.