Hybrid Deep Optimal Network for Recognizing Emotions Using Facial Expressions at Real Time

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Rakshith M. D. 1,* Harish H. Kenchannavar 2

1. Department of Computer Science and Engineering, Canara Engineering College, Benjanapadavu, Visvesvaraya Technological University, Belagavi, 590018, India

2. Department of Information Science and Engineering, Gogte Institute of Technology, Belagavi, Visvesvaraya Technological University, Belagavi, 590018, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2024.03.04

Received: 9 Dec. 2023 / Revised: 14 Jan. 2024 / Accepted: 9 Feb. 2024 / Published: 8 Jun. 2024

Index Terms

Facial Emotion Recognition, Adaptive Median Filter, Aquila Optimizer, Feature Extraction


Recognition of emotions by utilizing facial expressions is the progression of determining the various human facial emotions to infer the mental condition of the person. This recognition structure has been employed in several fields but more commonly applied in medical arena to determine psychological health problems. In this research work, a new hybrid model is projected using deep learning to recognize and classify facial expressions into seven emotions. Primarily, the facial image data is obtained from the datasets and subjected to pre-processing using adaptive median filter (AMF). Then, the features are extracted and facial emotions are classified through the improved VGG16+Aquila_BiLSTM (iVABL) deep optimal network. The proposed iVABL model provides accuracy of 95.63%, 96.61% and 95.58% on KDEF, JAFFE and Facial Expression Research Group 2D Database (FERG-DB) which is higher when compared to DCNN, DBN, Inception-V3, R-152 and Convolutional Bi-LSTM models. The iVABL model also takes less time to recognize the emotion from the facial image compared to the existing models.

Cite This Paper

Rakshith M. D., Harish H. Kenchannavar, "Hybrid Deep Optimal Network for Recognizing Emotions Using Facial Expressions at Real Time", International Journal of Intelligent Systems and Applications(IJISA), Vol.16, No.3, pp.47-58, 2024. DOI:10.5815/ijisa.2024.03.04


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