Dynamic Expression Recognition Based on Dynamic Time Warping and Active Appearance Model

XU Liangfeng; WANG Jiayong; CUI Jingnan; HU Min; ZHANG Keke; TENG Wendi

doi:10.11999/JEIT170416

Volume 40 Issue 2

Feb. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(2): 338-345

XU Liangfeng, WANG Jiayong, CUI Jingnan, HU Min, ZHANG Keke, TENG Wendi. Dynamic Expression Recognition Based on Dynamic Time Warping and Active Appearance Model[J]. Journal of Electronics & Information Technology, 2018, 40(2): 338-345. doi: 10.11999/JEIT170416

Citation:

XU Liangfeng, WANG Jiayong, CUI Jingnan, HU Min, ZHANG Keke, TENG Wendi. Dynamic Expression Recognition Based on Dynamic Time Warping and Active Appearance Model[J]. Journal of Electronics & Information Technology, 2018, 40(2): 338-345. doi: 10.11999/JEIT170416

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Dynamic Expression Recognition Based on Dynamic Time Warping and Active Appearance Model

doi: 10.11999/JEIT170416

1.
(School of Computer and Information, Hefei University of Technology, Hefei 230009, China)
2.
(School of Computer and Information, South China University of Technology, Guangzhou 510006, China)
3.
(School of Computer and Information, Hefei University of Technology, Hefei 230009, China)

Funds:

The National Natural Science Foundation of China (61300119, 61432004), The National Natural Science Foundation of Anhui Province (1408085MKL16)

Received Date: 2017-05-05
Rev Recd Date: 2017-11-08
Publish Date: 2018-02-19

Abstract

Abstract

To overcome the deficiency of static expression feature, which lacks time information and can not reflect the subtle changes of expression adequately, a dynamic expression recognition method is proposed for non-specific face: the dynamic expression recognition based on Dynamic Time Warping (DTW) and Active Appearance Model (AAM). Firstly, the method of DTW based on local gradient Dual Tree-Complex Wavelet Transform (DT-CWT) dominant direction pattern is used to warp expression sequence. Secondly, using AAM to locate 66 feature points of face image and track them. The changing feature of expression can be obtained by calculating the displacement of corresponding feature points in two adjacent expression sequences image. And using the feature points of neutral face to build the facial geometry model. The matching of facial geometry model can overcome the expression differences between various people. Finally, the nearest neighbor classifier is used for classification and recognition. The experimental results on CK+ database and HeFei University of Technology-Face Emotion (HFUT-FE) database show that the proposed algorithm has a high degree of accuracy.

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References(25)

References

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