Facial Expression Recognition Based on the Fusion of Spatio-temporal Features in Video Sequences

WANG Xiaohua; XIA Chen; HU Min; REN Fuji

doi:10.11999/JEIT170592

Volume 40 Issue 3

Mar. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(3): 626-632

WANG Xiaohua, XIA Chen, HU Min, REN Fuji. Facial Expression Recognition Based on the Fusion of Spatio-temporal Features in Video Sequences[J]. Journal of Electronics & Information Technology, 2018, 40(3): 626-632. doi: 10.11999/JEIT170592

Citation:

WANG Xiaohua, XIA Chen, HU Min, REN Fuji. Facial Expression Recognition Based on the Fusion of Spatio-temporal Features in Video Sequences[J]. Journal of Electronics & Information Technology, 2018, 40(3): 626-632. doi: 10.11999/JEIT170592

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Facial Expression Recognition Based on the Fusion of Spatio-temporal Features in Video Sequences

doi: 10.11999/JEIT170592

Funds:

The National Natural Science Foundation of China (61672202, 61432004, 61300119), The National Natural Science Foundation of China -Shenzhen Joint Foundation (Key Project) (U1613217), Open foundation of ?The Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province (JSWLW-2017-017)

Received Date: 2017-06-20
Rev Recd Date: 2017-11-28
Publish Date: 2018-03-19

Abstract

Abstract

For facial expression recognition based on video sequences, the changing information of facial regions along the time axis can be described by dynamic descriptors more effectively than static descriptors. This paper proposes an expression recognition method based on the dynamic texture and motion information, learning from the principle of Local Binary Pattern on Three Orthogonal Planes (LBP-TOP), Spatio-Temporal Weber Local Descriptor (STWLD) is proposed to describe the dynamic texture feature information of the facial expression sequence. Moreover, using Block-based Histogram of Optical Flow features (BHOF), the motion information can be described. Through the combination of the dynamic texture and motion information, and finally SVM is applied to complete the expression classification. The results of the cross experiments on the CK + and MMI expression database show that the method achieves better performance than methods using the single descriptors. The comparison experiments with other related methods also prove the superiority of the method.
- Video sequences,
- Expression recognition,
- Spatio-Temporal Weber Local Descriptor (STWLD),
- Block- based Histogram of Optical Flow (BHOF)

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References(24)

References

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