A geometric and fractional entropy-based method for family photo classification

Kaljahi, Maryam Asadzadeh and Shivakumara, Palaiahnakote and Hu, Tianping and Jalab, Hamid Abdullah and Ibrahim, Rabha Waell and Blumenstein, Michael and Lu, Tong and Ayub, Mohamad Nizam (2019) A geometric and fractional entropy-based method for family photo classification. Expert Systems with Applications: X, 3. p. 100008. ISSN 2590-1885, DOI https://doi.org/10.1016/j.eswax.2019.100008.

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Official URL: https://doi.org/10.1016/j.eswax.2019.100008


Due to the power and impact of social media, unsolved practical issues such as human trafficking, kinship recognition, and clustering family photos from large collections have recently received special attention from researchers. In this paper, we present a new idea for family and non-family photo classification. Unlike existing methods that explore face recognition and biometric features, the proposed method explores the strengths of facial geometric features and texture given by a new fractional-entropy approach for classification. The geometric features include spatial and angle information of facial key points, which give spatial and directional coherence. The texture features extract regular patterns in images. The proposed method then combines the above properties in a new way for classifying family and non-family photos with the help of Convolutional Neural Networks (CNNs). Experimental results on our own as well as benchmark datasets show that the proposed approach outperforms the state-of-the-art methods in terms of classification rate. © 2019

Item Type: Article
Uncontrolled Keywords: Face recognition; Facial points; Facial geometric features; Fractional entropy; Convolutional neural networks; Family photo classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Apr 2020 05:11
Last Modified: 08 Apr 2020 05:11
URI: http://eprints.um.edu.my/id/eprint/24168

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