Ramachandran Vedantham; Ravisankar Malladi; Sivaiah Bellamkonda; Edara Sreenivasa Reddy

Disorder Facial Emotion Recognition with Grey Wolf Optimized Gefficient-Net for Autism Spectrum

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Author(s)

Ramachandran Vedantham ^1,* Ravisankar Malladi ² Sivaiah Bellamkonda ³ Edara Sreenivasa Reddy ⁴

1. Department of CSE, Vasireddy Venkatadri Institute of Technology, Nambur, Andhra Pradesh, 522508, India

2. Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India

3. Department of CSE, Indian Institute of Information Technology, Kottayam, Kerala, 686635, India

4. ANU College of Engineering & Technology, Acharya Nagarjuna University, Nambur, Andhra Pradesh, 522510, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2025.01.01

Received: 29 May 2024 / Revised: 26 Jun. 2024 / Accepted: 15 Aug. 2024 / Published: 8 Feb. 2025

Index Terms

Autism Spectrum, Disorder, Facial Expression Recognition, Gefficient-Net, Grey Wolf Optimization, Feature Extraction, VGG-16

Abstract

Autism spectrum disorder (ASD) is a neurological issue that impacts brain function at an earlier stage. The autistic person realizes several complexities in communication or social interaction. ASD detection from face images is complicated in the field of computer vision. In this paper, a hybrid GEfficient-Net with a Gray-Wolf (GWO) optimization algorithm for detecting ASD from facial images is proposed. The proposed approach combines the advantages of both EfficientNet and GoogleNet. Initially, the face image from the dataset is pre-processed, and the facial features are extracted with the VGG-16 feature extraction technique. It extracts the most discriminative features by learning the representation of each network layer. The hyperparameters of GoogleNet are optimally selected with the GWO algorithm. The proposed approach is uniformly scaled in all directions to enhance performance. The proposed approach is implemented with the Autistic children’s face image dataset, and the performance is computed in terms of accuracy, sensitivity, specificity, G-mean, etc. Moreover, the proposed approach improves the accuracy to 0.9654 and minimizes the error rate to 0.0512. The experimental outcomes demonstrate the proposed ASD diagnosis has achieved better performance.

Cite This Paper

Ramachandran Vedantham, Ravisankar Malladi, Sivaiah Bellamkonda, Edara Sreenivasa Reddy, "Disorder Facial Emotion Recognition with Grey Wolf Optimized Gefficient-Net for Autism Spectrum", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.17, No.1, pp. 1-16, 2025. DOI:10.5815/ijigsp.2025.01.01

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International Journal of Image, Graphics and Signal Processing (IJIGSP)