A geometric and fractional entropy-based method for family photo classification
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 c...
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| Format: | Article |
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Elsevier
2019
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| _version_ | 1825722198497689600 |
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| author | Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Hu, Tianping Jalab, Hamid Abdullah Ibrahim, Rabha Waell Blumenstein, Michael Lu, Tong Ayub, Mohamad Nizam |
| author_facet | Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Hu, Tianping Jalab, Hamid Abdullah Ibrahim, Rabha Waell Blumenstein, Michael Lu, Tong Ayub, Mohamad Nizam |
| author_sort | Kaljahi, Maryam Asadzadeh |
| collection | UM |
| description | 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 |
| first_indexed | 2024-03-06T06:02:00Z |
| format | Article |
| id | um.eprints-24168 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T06:02:00Z |
| publishDate | 2019 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-241682020-04-08T05:11:12Z http://eprints.um.edu.my/24168/ A geometric and fractional entropy-based method for family photo classification Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Hu, Tianping Jalab, Hamid Abdullah Ibrahim, Rabha Waell Blumenstein, Michael Lu, Tong Ayub, Mohamad Nizam QA75 Electronic computers. Computer science 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 Elsevier 2019 Article PeerReviewed 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 <https://doi.org/10.1016/j.eswax.2019.100008>. https://doi.org/10.1016/j.eswax.2019.100008 doi:10.1016/j.eswax.2019.100008 |
| spellingShingle | QA75 Electronic computers. Computer science Kaljahi, Maryam Asadzadeh Shivakumara, Palaiahnakote Hu, Tianping Jalab, Hamid Abdullah Ibrahim, Rabha Waell Blumenstein, Michael Lu, Tong Ayub, Mohamad Nizam A geometric and fractional entropy-based method for family photo classification |
| title | A geometric and fractional entropy-based method for family photo classification |
| title_full | A geometric and fractional entropy-based method for family photo classification |
| title_fullStr | A geometric and fractional entropy-based method for family photo classification |
| title_full_unstemmed | A geometric and fractional entropy-based method for family photo classification |
| title_short | A geometric and fractional entropy-based method for family photo classification |
| title_sort | geometric and fractional entropy based method for family photo classification |
| topic | QA75 Electronic computers. Computer science |
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