A Facial Expression Recognition Method Using Improved Capsule Network Model

Aiming at the problem of facial expression recognition under unconstrained conditions, a facial expression recognition method based on an improved capsule network model is proposed. Firstly, the expression image is normalized by illumination based on the improved Weber face, and the key points of the face are detected by the Gaussian process regression tree. Then, the 3dmms model is introduced. The 3D face shape, which is consistent with the face in the image, is provided by iterative estimation so as to further improve the image quality of face pose standardization. In this paper, we consider that the convolution features used in facial expression recognition need to be trained from the beginning and add as many different samples as possible in the training process. Finally, this paper attempts to combine the traditional deep learning technology with capsule configuration, adds an attention layer after the primary capsule layer in the capsule network, and proposes an improved capsule structure model suitable for expression recognition. The experimental results on JAFFE and BU-3DFE datasets show that the recognition rate can reach 96.66% and 80.64%, respectively.

[1]  Zhang Rui,et al.  Key point location method for pedestrians in depth images based on deep learning , 2019 .

[2]  Ashim Saha,et al.  Facial expression recognition based on eigenspaces and principle component analysis , 2018, Int. J. Comput. Vis. Robotics.

[3]  Huagang Liang,et al.  Deep convolutional BiLSTM fusion network for facial expression recognition , 2019, The Visual Computer.

[4]  Wen Hao,et al.  Expression-insensitive 3D face recognition by the fusion of multiple subject-specific curves , 2018, Neurocomputing.

[5]  Fengping An,et al.  Facial expression recognition algorithm based on parameter adaptive initialization of CNN and LSTM , 2019, The Visual Computer.

[6]  Guang Yang,et al.  Multiple features fusion for facial expression recognition based on ELM , 2018, Int. J. Embed. Syst..

[7]  Kaspars Sudars.  Face recognition Face2vec based on deep learning: Small database case , 2017, Automatic Control and Computer Sciences.

[8]  Radhouane Guermazi,et al.  Facial-expression recognition based on a low-dimensional temporal feature space , 2017, Multimedia Tools and Applications.

[9]  Ahmed A. Nashat,et al.  Facial expression recognition using best tree RD-LGP encoded features and HMM , 2018, Int. J. Wavelets Multiresolution Inf. Process..

[10]  Mahesh U Nagaral,et al.  Hybrid Approach for Facial Expression Recognition using HJDLBP and LBP Histogram in Video Sequences , 2018 .

[11]  K. T. Ramesh,et al.  An improved normalization technique for white light photoelasticity , 2018, Optics and Lasers in Engineering.

[12]  Javier Ruiz-del-Solar,et al.  A comparative study of thermal face recognition methods in unconstrained environments , 2012, Pattern Recognit..

[13]  Rehab F. Abdel-Kader,et al.  Plane detection in 3D point cloud using octree-balanced density down-sampling and iterative adaptive plane extraction , 2018, IET Image Process..

[14]  Jiafu Su,et al.  A Multi-Objective Optimization Model for Green Supply Chain Considering Environmental Benefits , 2019, Sustainability.

[15]  Weihua Cao,et al.  Combining 2D Gabor and Local Binary Pattern for Facial Expression Recognition Using Extreme Learning Machine , 2019, J. Adv. Comput. Intell. Intell. Informatics.

[16]  Phill-Kyu Rhee,et al.  Wild facial expression recognition based on incremental active learning , 2018, Cognitive Systems Research.

[17]  Chokri Ben Amar,et al.  MLP neural network using constructive training algorithm: application to face recognition and facial expression recognition , 2017 .

[18]  Shiqing Zhang,et al.  Facial Expression Recognition via Deep Learning , 2015 .

[19]  Long Ye,et al.  Identifying facial expression using adaptive sub-layer compensation based feature extraction , 2017, J. Vis. Commun. Image Represent..

[20]  Shiqing Zhang,et al.  Robust facial expression recognition using improved sparse classifier , 2015, Int. J. Comput. Appl. Technol..

[21]  Hung-Hsu Tsai,et al.  Facial expression recognition using a combination of multiple facial features and support vector machine , 2018, Soft Comput..

[22]  Yongzhao Zhan,et al.  Face detection based on multilayer feed‐forward neural network and Haar features , 2018, Softw. Pract. Exp..

[23]  Guangcan Liu,et al.  Deeper cascaded peak-piloted network for weak expression recognition , 2018, The Visual Computer.

[24]  Anil K. Jain,et al.  Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection , 2014, IEEE Transactions on Information Forensics and Security.

[25]  Haitao Zhao,et al.  An efficient unconstrained facial expression recognition algorithm based on Stack Binarized Auto-encoders and Binarized Neural Networks , 2017, Neurocomputing.

[26]  Xiaohui Yuan,et al.  Conditional convolution neural network enhanced random forest for facial expression recognition , 2018, Pattern Recognit..

[27]  Gongfa Li,et al.  CNN-Based Facial Expression Recognition from Annotated RGB-D Images for Human-Robot Interaction , 2019, Int. J. Humanoid Robotics.

[28]  Qi Hu,et al.  RGB-D Image Multi-Target Detection Method Based on 3D DSF R-CNN , 2019, Int. J. Pattern Recognit. Artif. Intell..