Open Access

ARTICLE

Face Recognition System Using Deep Belief Network and Particle Swarm Optimization

K. Babu^1,*, C. Kumar², C. Kannaiyaraju³

1 Department of Computer Science and Engineering, Sri Rangapoopathi College of Engineering, Gingee, 604151, India
2 Department of Electrical and Electronics Engineering, M. Kumarasamy College of Engineering, Karur, 639113, India
3 Department of Computer Science, Ambo University of Technology, Ambo, 00251, Ethiopia

* Corresponding Author: K. Babu. Email:

Intelligent Automation & Soft Computing 2022, 33(1), 317-329. https://doi.org/10.32604/iasc.2022.023756

Received 20 September 2021; Accepted 05 November 2021; Issue published 05 January 2022

Abstract

Facial expression for different emotional feelings makes it interesting for researchers to develop recognition techniques. Facial expression is the outcome of emotions they feel, behavioral acts, and the physiological condition of one’s mind. In the world of computer visions and algorithms, precise facial recognition is tough. In predicting the expression of a face, machine learning/artificial intelligence plays a significant role. The deep learning techniques are widely used in more challenging real-world problems which are highly encouraged in facial emotional analysis. In this article, we use three phases for facial expression recognition techniques. The principal component analysis-based dimensionality reduction techniques are used with Eigen face value for edge detection. Then the feature extraction is performed using swarm intelligence-based grey wolf with particle swarm optimization techniques. The neural network is highly used in deep learning techniques for classification. Here we use a deep belief network (DBN) for classifying the recognized image. The proposed method’s results are assessed using the most comprehensive facial expression datasets, including RAF-DB, AffecteNet, and Cohn-Kanade (CK+). This developed approach improves existing methods with the maximum accuracy of 94.82%, 95.34%, 98.82%, and 97.82% on the test RAF-DB, AFfectNet, CK+, and FED-RO datasets respectively.

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Keywords

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