A novel weighted deep convolution model – African vultures optimization algorithm for an automated facial emotion recognition system

• Published in: Multimedia tools and applications Vol. 83; no. 6; pp. 18607 - 18636
• Main Authors: M, Sumithra, N, Rajkumar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Automation; Computer Communication Networks; Computer Science; Convolution; Data Structures and Information Theory; Datasets; Deep learning; Emotion recognition; Emotions; Face recognition; Feature recognition; Image quality; Machine learning; Multimedia Information Systems; Optimization; Optimization algorithms; Special Purpose and Application-Based Systems; Track 6: Computer Vision for Multimedia Applications; Feature extraction; African Vultures Optimization Algorithm (AVOA); Weighted Deep Convolution Model (WDCM); Face emotion recognition; Machine learning; Automatic Direct Face Filtering (ADFF)
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-17638-2

Facial emotion recognition plays a vital role in the field of human-computer interaction, since the communication is significantly influenced by emotion. The conventional deep learning techniques have the unique issues with computing burden, high requirements, and system complexity, deep learning has relatively limited application. Therefore, the proposed work intends to utilize a novel as well as computationally effective deep learning model for an automated facial expression recognition system. Here, an Automatic Direct Face Filtering (ADFF) method is used to filter, remove noise, and improve the quality of the face image. By using the components of the face’s features, a unique Weighted Deep Convolution Model (WDCM) technique is used to precisely predict the emotion from the face image. Furthermore, an African Vultures Optimization Algorithm (AVOA) is used to optimize the number of features in order to streamline the recognition process with minimal computational load and time, improving forecast accuracy. The inclusion of ADFF and AVOA are the major reasons for obtaining the better classification performance in the proposed, because it boosts the training and testing performance of the WDCM with low time consumption and high processing speed by providing the best optimal features for recognition. Moreover, the performance and results of the proposed WDCM-AVOA technique is validated and compared using the popular JAFEE and CK + datasets. By using the proposed framework, the overall average recognition accuracy is improved up to 99% and the average prediction is boosted to 99.2% for both datasets.