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Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network
Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, Jharkhand, India
* Corresponding Author: Vani A. Hiremani. Email:
Computer Systems Science and Engineering 2023, 44(3), 2603-2618. https://doi.org/10.32604/csse.2023.027612
Received 21 January 2022; Accepted 06 April 2022; Issue published 01 August 2022
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The inter-class face classification problem is more reasonable than the intra-class classification problem. To address this issue, we have carried out empirical research on classifying Indian people to their geographical regions. This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India, referring to human vision. We have created an Automated Human Intelligence System (AHIS) to evaluate human visual capabilities. Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features. We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy. The proposed model achieved mean F1 and Matthew Correlation Coefficient (MCC) of 0.92 and 0.84, respectively, on the validation set, outperforming the traditional Convolutional Neural Network (CNN). The CNN-Contoured Face (CNN-FC) model is developed to train contoured face images to investigate the influence of face shape. Finally, to cross-validate the accuracy of these models, the traditional CNN model is trained on the same dataset. With an accuracy of 92.98%, the Modified-CNN (M-CNN) model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems. A novel Indian regional face dataset is created for supporting this supervised classification work, and it will be available to the research community.Keywords
Data collection and preparation; human vision analysis; machine vision; canny edge approximation method; color local binary patterns; convolutional neural network
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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