Tongtong Cao
ABSTRACT
Aiming at the problems of low recognition rate and slow training speed of facial expression recognition method in the background of complex images, an improved facial expression recognition algorithm based on convolutional neural networks is proposed. The proposed algorithm introduces K-Means clustering idea and SVM classifier in the framework of convolutional neural network. Firstly, the algorithm trains the K-Means clustering model by using the label-free expression images, and selects the K-means clustering centers with good data characteristics, which are used as the initial value of the convolution kernel of the CNN model to extract features. Secondly, using the feature extraction processing of the convolutional neural network, the extracted features are fed to the multi-class SVM classifier. The experimental results show that the proposed method reduces the training time of the model overall, improves the accuracies of facial expression recognition under the background of complex images, and has a certain robustness.
- Li W, Li M, Su Z, et al. A deep-learning approach to facial expression recognition with candid images{C}// Iapr International Conference on Machine Vision Applications. IEEE, 2015:279--282.Google Scholar
- Ige E O, Debattista K, Chalmers A. Towards HDR Based Facial Expression Recognition under Complex Lighting{C}// Computer Graphics International. ACM, 2016:49--52. Google ScholarDigital Library
- Al-Sumaidaee S A M, Dlay S S, Woo W L, et al. Facial expression recognition using local Gabor gradient code-horizontal diagonal descriptor{C}// Iet International Conference on Intelligent Signal Processing. IET, 2016.Google Scholar
- Song Huansheng, Zhang Xiangqing, Zheng Baofeng, et al. Vehicle target detection in complex scenes based on deep learning method{J}. Journal of Computer Applications, 2018(4).Google Scholar
- Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks{C}// Advances in Neural Information Processing Systems. 2012: 1106--1114. Google ScholarDigital Library
- Tang Hao, Huang Weipeng, Li Zheyuan, et al. Negative expression recognition method based on improved convolutional neural network{J}. Journal of Huazhong University of Science and Technology(Natural Science), 2015(s1):457--460.Google Scholar
- Li H, Zhe L, Shen X, et al. A convolutional neural network cascade for face detection{C}// Computer Vision & Pattern Recognition. 2015.Google Scholar
- Yu Z, Zhang C. Image based Static Facial Expression Recognition with Multiple Deep Network Learning{C}// ACM on International Conference on Multimodal Interaction. ACM, 2015:435--442. Google ScholarDigital Library
- Coate A, Ng A Y, Lee H. An analysis of single-layer networks in unsupervised feature learning{C}. International Conference on Artificial Intelligence and Statistics, 2011: 215--223.Google Scholar
- Gu M. Convolution Neural Networks Embedding K-means{J}. Journal of Information & Computational Science, 2015, 12(17):6391--6400.Google ScholarCross Ref
- Zhang L, Tjondronegoro D, Chandran V. Facial expression recognition experiments with data from television broadcasts and the World Wide Web*{J}. Image & Vision Computing, 2014, 32(2):107--119. Google ScholarDigital Library
- Simonyan k, Zisserman A. Very deep convolutional networks for large-scale image recognition{J}. arXiv preprint arXiv:1409. 1556, 2014.Google Scholar
- Karam L J, Zhu T. Quality labeled faces in the wild (QLFW): a database for studying face recognition in real-world environments{J}. Proceedings of SPIE - The International Society for Optical Engineering, 2015, 9394:93940B-93940B-10.Google Scholar
Index Terms
- Facial Expression Recognition Algorithm Based on the Combination of CNN and K-Means
Recommendations
Person-independent facial expression recognition method based on improved Wasserstein generative adversarial networks in combination with identity aware
AbstractSince the distinction between two expressions is fairly vague, usually a subtle change in one part of the human face is enough to change a facial expression. Most of the existing facial expression recognition algorithms are not robust enough ...
Image ratio features for facial expression recognition application
Special issue on game theoryVideo-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised ...
A simple approach to facial expression recognition
CEA'07: Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and ApplicationsHuman face-to-face communication plays an important role in human communication and interaction. In recent years, several different approaches have been proposed for developing methods of automatic facial expression analysis. In this paper, a simple ...
Comments