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Developing real-time training dataset for human racial classification

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Abstract

In every image processing venture the quality of data sample collected and the processing techniques poses direct impact on the result. This work explains development of these vital phases in the context to exploit it for racial classification. Here we propose a novel Indian regional face database (IRFD) consisting of large set distinctive face images of north, east, west and south regions of India to mitigate the scarcity of regional and labeled face images for future supervised classification process. The face images are collected from different universities and acquired through both online and offline mode. Due to this discrepancy the face database is exposed to challenges like varying dimension size, non-uniform background, low resolution, illumination, and pose variation. In view of addressing these problems we have proposed competent image processing techniques to enhance the quality of images. Varying size and low resolution were the main issues among others encountered while training Convolutional Neural Network (CNN) model. To handle this we have developed an expeditious compression algorithm which would reduce large size of all images to ±97% less in size without compromising the quality. Further to enhance low quality images we have proposed brightness and contrast adjusting algorithm. The efficiency of this quantitative and qualitative data set is evaluated against CNN model which has yielded ±88.21% accuracy under racial classification.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Birla Institute of Technology, Mesra, Ranchi, India

    Vani A. Hiremani & Kishore Kumar Senapati

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  1. Vani A. Hiremani
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  2. Kishore Kumar Senapati
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Correspondence to Vani A. Hiremani.

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Appendix 1

Appendix 1

Table 10 Samples with original size and new dimension after passing through proposed compression method
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Hiremani, V.A., Senapati, K.K. Developing real-time training dataset for human racial classification. Multimed Tools Appl 81, 28103–28127 (2022). https://doi.org/10.1007/s11042-022-12947-4

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  • DOI: https://doi.org/10.1007/s11042-022-12947-4

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