Title: Effect of layers on CNN model accuracy for facial emotion recognition

Authors: M.D. Rakshith; Harish H. Kenchannavar

Addresses: Department of Computer Science and Engineering, Canara Engineering College, Benjanapadavu, Karnataka-574219, India; Visvesvaraya Technological University, Belagavi, Karnataka-590018, India ' Department of Information Science and Engineering, KLS's Gogte Institute of Technology, Belagavi, Karnataka-590008, India; Visvesvaraya Technological University, Belagavi, Karnataka-590018, India

Abstract: Facial expression recognition has become a very tedious task in the domain of image recognition. Image classification involves drastic usage of deep learning techniques. This has resulted in the increased usage of convolutional neural networks (CNNs) for recognising emotions through facial expressions. In deep learning, developing the compact network architecture that achieves high accuracy on the data of interest is a significant challenge. In the presented article, a novel optimised CNN (O-CNN) model consisting of five convolution layers is proposed and the effect of layers on the test accuracy is observed on FER-2013 dataset. The hyperparameters of CNN such as kernel size, number of kernels, activation function, dropout, number of hidden units, batch size and epochs are considered for experimentation. By keeping the constant kernel size, the convolution layers and kernels are varied for the model evaluation. The test accuracy obtained by the O-CNN model on FER2013 dataset without batch normalization and for 50 epochs is 64.17%.

Keywords: facial expression; convolutional neural network; CNN; hyperparameters; deep learning.

DOI: 10.1504/IJCVR.2025.149825

International Journal of Computational Vision and Robotics, 2025 Vol.15 No.6, pp.669 - 681

Received: 27 Nov 2022
Accepted: 25 Nov 2023
Published online: 14 Nov 2025 *

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