FER based on the improved convex nonnegative matrix factorization feature
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FER based on the improved convex nonnegative matrix factorization feature Jing Zhou 1 & Tianjiang Wang 2 Received: 30 January 2019 / Revised: 14 February 2020 / Accepted: 7 April 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Facial expression recognition is an important research issue in the pattern recognition field. In this paper, we intend to present an accurate facial expression recognition (FER) system, which employs an improved convex non-negative matrix factorization (ICNMF) method based on a novel objective function and smaller iterative step sizes for feature extraction. Since negative values appearing in the facial expression feature will weaken the features and reduce the recognition rate, the nonnegative matrix factorization (NMF) methods are adopted to guarantee the non-negativity of the extracted feature value to improve the recognition rate. To enhance the performance of NMF method for FER, the ICNMF approach based on a novel convergent objective function and smaller iterative step sizes is proposed, and the FER rate can be improved effectively. In the FER system, the face region is detected firstly, and is enhanced by histogram specification, secondly the ICNMF approach is adopted to extract features and then the feature coefficient matrix is achieved. Finally, the SVM classifier is applied to recognize the extracted features. To validate the effectiveness of FER system, four public available datasets of MultiPIE, CK+ , FER2013 and SFEW are tested and then high recognition rates can be achieved based on ICNMF method. In addition, the proposed ICNMF approach is compared with the methods of multi-layer NMF, sparse non-negative matrix factorization (SNMF), the traditional convex non-negative matrix factorization (CNMF), deep belief networks (DBN) and stacked auto-encoder (SAE), and results of experiments show that the proposed ICNMF approach is significantly effective contrasting to the other five expression extraction methods. Keywords Facial expression recognition . Feature extraction . Improved convex non-negative matrix factorization . Novel objective function . Improved iterative step sizes
* Jing Zhou [email protected]
1
School of Mathematics and Computer Science, Jianghan University, Wuhan 430056 Hubei, China
2
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 Hubei, China
Multimedia Tools and Applications
1 Introduction Pattern recognition method plays a significant role in human interaction and artificial intelligence, and the facial expression recognition is a very challenging research topic in pattern recognition field [1, 9]. Since the differences of features caused by facial expression changing are subtle, it is very difficult to recognize them automatically. Thus researchers work hard to explore methods to recognize the expression accurately and developed some expression recognition systems [4, 21, 28, 30]. A complete facial expression recognition system generally includes face detection a
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