Failure Detection and Correction for Appearance Based Facial Tracking

WANG Lei; LIANG Yixiong; CAI Wangyang; ZOU Beiji

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Article Navigation > Chinese Journal of Electronics > 2015 > 24(1): 20-25

WANG Lei, LIANG Yixiong, CAI Wangyang, et al., “Failure Detection and Correction for Appearance Based Facial Tracking,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 20-25, 2015,

Citation:

WANG Lei, LIANG Yixiong, CAI Wangyang, et al., “Failure Detection and Correction for Appearance Based Facial Tracking,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 20-25, 2015,

WANG Lei, LIANG Yixiong, CAI Wangyang, et al., “Failure Detection and Correction for Appearance Based Facial Tracking,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 20-25, 2015,

Citation:

WANG Lei, LIANG Yixiong, CAI Wangyang, et al., “Failure Detection and Correction for Appearance Based Facial Tracking,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 20-25, 2015,

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Failure Detection and Correction for Appearance Based Facial Tracking

1.
Xiangya Hospital, Central South University, Changsha 410008, China;
2.
School of Information Science & Engineering, Central South University, Changsha 410083, China;
3.
School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China

Funds: This work is supported by the Hunan Provincial Natural Science Foundation of China (No.09JJ6102), Natural Science Foundation of China (No.61173122) and Key Project of Natural Science Foundation of Hunan Province, China (No.12JJ2038).

More Information

Corresponding author: ZOU Beiji received the B.E. degree in computer science from the Zhejiang University, Hangzhou, China, in 1982 and the Ph.D. degree in control theory & control engineering from Hunan University, Changsha, China, in 2001. Since 2004, he has been with the School of Information Science & Engineering, Central South University, Changsha, China, where he is currently a professor. His research interests include machine learning and computer vision. (Email: bjzou@vip.163.com)
Received Date: 2013-07-01
Rev Recd Date: 2014-05-01
Publish Date: 2015-01-10

Abstract

Abstract

The appearance based facial tracking methods, such as active appearance models and candide models, are widely used in intelligent user interface and facial expression recognition. This paper proposes a novel method to detect and correct the failures in appearance based facial tracking. A sparse coding strategy is applied to learn an efficient feature representation for the difference between the warped image and the face template. The features are extracted by directly project the difference image to the space spanned by the dictionary of the sparse coding. An iterative regression based method is proposed to detect and correct the failures according to the features. Experimental evaluation on an open dataset shows a global performance improvement of the tracking algorithm.
- Facial tracking,
- Failure detection,
- Failure correction,
- Sparse coding

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References(18)

References

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