Facial Expression Recognition Method Based on Multi-scale Detail Enhancement

Xiaohui TAN; Zhaowei LI; Yachun FAN

doi:10.11999/JEIT181088

Volume 41 Issue 11

Nov. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(11): 2752-2759

Xiaohui TAN, Zhaowei LI, Yachun FAN. Facial Expression Recognition Method Based on Multi-scale Detail Enhancement[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2752-2759. doi: 10.11999/JEIT181088

Citation:

Xiaohui TAN, Zhaowei LI, Yachun FAN. Facial Expression Recognition Method Based on Multi-scale Detail Enhancement[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2752-2759. doi: 10.11999/JEIT181088

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Facial Expression Recognition Method Based on Multi-scale Detail Enhancement

doi: 10.11999/JEIT181088

Xiaohui TAN^{1, 3},
Zhaowei LI^{1, 4},
Yachun FAN^{2
,
,}

1.
College of Information Engineering, Capital Normal University, Beijing 100048, China
2.
College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
3.
Beijing Key Laboratory of Electronic System Reliability and Prognostics, Capital Normal University, Beijing 100048, China
4.
Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing 100048, China

Funds: The National Key R&D Program of China(2017YFB1002804), The National Natural Science Foundation of China(61602324), The Open Project of State Key Lab of CAD & CG, Zhejiang University(A1914)

Received Date: 2018-11-26
Rev Recd Date: 2019-02-27

Available Online: 2019-05-20

Publish Date: 2019-11-01

Abstract

Abstract

Facial expression is the most intuitive description of changes in psychological emotions, and different people have great differences in facial expressions. The existing facial expression recognition methods use facial statistical features to distinguish among different expressions, but these methods are short of deep exploration for facial detail information. According to the definition of facial behavior coding by psychologists, it can be seen that the local detail information of the face determines the meaning of facial expression. Therefore, a facial expression recognition method based on multi-scale detail enhancement is proposed, because facial expression is much more affected by the image details than other information, the method proposed in this paper extracts the image detail information with the Gaussian pyramid firstly, thus the image is enhanced in detail to enrich the facial expression information. Secondly, for the local characteristics of facial expressions, a local gradient feature calculation method is proposed based on hierarchical structure to describe the local shape features of facial feature points. Finally, facial expressions are classified using a Support Vector Machine (SVM). The experimental results in the CK+ expression database show that the method not only proves the important role of image detail in facial expression recognition, but also obtains very good recognition results under small-scale training data. The average recognition rate of expressions reaches 98.19%.
- Expression recognition,
- Image pyramid,
- Gauss difference,
- Detail enhancement,
- Support Vector Machine(SVM)

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References(17)

References

MEHRABIAN A. Communication without words[J]. Psychology Today, 1968, 2(4): 53–56.

GUO Zhenhua, ZHANG Lei, and ZHANG D. A completed modeling of local binary pattern operator for texture classification[J]. IEEE Transactions on Image Processing, 2010, 19(6): 1657–1663. doi: 10.1109/TIP.2010.2044957

王玮, 黄非非, 李见为, 等. 采用LBP金字塔的人脸描述与识别[J]. 计算机辅助设计与图形学学报, 2009, 21(1): 94–100, 106.

WANG Wei, HUANG Feifei, LI Jianwei, et al. Face description and recognition by LBP pyramid[J]. Journal of Computer-Aided Design &Computer Graphics, 2009, 21(1): 94–100, 106.

钟思志. 人脸面部表情识别算法研究[D]. [硕士论文], 华东师范大学, 2015.

ZHONG Sizhi. Research on facial expression recognition[D]. [Master dissertation], East China Normal University, 2015.

杨凡, 张磊. 基于Gabor参数矩阵与改进Adaboost的人脸表情识别[J]. 计算机应用, 2014, 34(4): 1134–1138.

YANG Fan and ZHANG Lei. Facial expression recognition based on Gabor parameters matrix and improved Adaboost[J]. Journal of Computer Applications, 2014, 34(4): 1134–1138.

童莹. 基于空间多尺度HOG特征的人脸表情识别方法[J]. 计算机工程与设计, 2014, 35(11): 3918–3922, 3979. doi: 10.3969/j.issn.1000-7024.2014.11.041

TONG Ying. Facial expression recognition algorithm based on spatial multi-scaled HOG feature[J]. Computer Engineering and Design, 2014, 35(11): 3918–3922, 3979. doi: 10.3969/j.issn.1000-7024.2014.11.041

TURAN C and LAM K M. Region-based feature fusion for facial-expression recognition[C]. Proceedings of 2014 IEEE International Conference on Image Processing, Paris, France, 2014: 5966–5970.

SAEED A, AL-HAMADI A, and NIESE R. The effectiveness of using geometrical features for facial expression recognition[C]. Proceedings of 2013 IEEE International Conference on Cybernetics, Lausanne, Switzerland, 2013: 122–127.

CHEN Junkai, CHEN Zenghai, CHI Zheru, et al. Facial expression recognition in video with multiple feature fusion[J]. IEEE Transactions on Affective Computing, 2018, 9(1): 38–50. doi: 10.1109/TAFFC.2016.2593719

任福继, 于曼丽, 胡敏, 等. 融合表情和BVP生理信号的双模态视频情感识别[J]. 中国图象图形学报, 2018, 23(5): 688–697.

REN Fuji, YU Manli, HU Min, et al. Dual-modality video emotion recognition based on facial expression and BVP physiological signal[J]. Journal of Image and Graphics, 2018, 23(5): 688–697.

LOPES A T, DE AGUIAR E, DE SOUZA A F, et al. Facial expression recognition with convolutional neural networks: Coping with few data and the training sample order[J]. Pattern Recognition, 2017, 61: 610–628. doi: 10.1016/j.patcog.2016.07.026

ZHANG Chongsheng, WANG Pengyou, CHEN Ke, et al. Identity-aware convolutional neural networks for facial expression recognition[J]. Journal of Systems Engineering and Electronics, 2017, 28(4): 784–792.

LUCEY P, COHN J F, KANADE T, et al. The extended cohn-kanade dataset (CK+): A complete dataset for action unit and emotion-specified expression[C]. Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, San Francisco, USA, 2010: 94–101.

YAO Yongqiang, HUANG Di, YANG Xudong, et al. Texture and geometry scattering representation-based facial expression recognition in 2D+3D videos[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 2018, 14(1S): 18.

李勇, 林小竹, 蒋梦莹. 基于跨连接LeNet-5网络的面部表情识别[J]. 自动化学报, 2018, 44(1): 176–182.

LI Yong, LIN Xiaozhu, and JIANG Mengying. Facial expression recognition with cross-connect LeNet-5 network[J]. Acta Automatica Sinica, 2018, 44(1): 176–182.

JIA Qi, GAO Xinkai, GUO He, et al. Multi-layer sparse representation for weighted LBP-patches based facial expression recognition[J]. Sensors, 2015, 15(3): 6719–6739. doi: 10.3390/s150306719

ZHOU Jun, ZHANG Sue, MEI Hongyan, et al. A method of facial expression recognition based on Gabor and NMF[J]. Pattern Recognition and Image Analysis, 2016, 26(1): 119–124. doi: 10.1134/S1054661815040070

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