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Symmetric Uncertainty Based Search Space Reduction for Fast Face Recognition

Symmetric Uncertainty Based Search Space Reduction for Fast Face Recognition

C. Sweetlin Hemalatha, Vignesh Sankaran, Vaidehi V, Shree Nandhini S, Sharmi P, Lavanya B, Vasuhi S, Ranajit Kumar
Copyright: © 2018 |Volume: 14 |Issue: 4 |Pages: 21
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522542810|DOI: 10.4018/IJIIT.2018100105
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MLA

Hemalatha, C. Sweetlin, et al. "Symmetric Uncertainty Based Search Space Reduction for Fast Face Recognition." IJIIT vol.14, no.4 2018: pp.77-97. http://doi.org/10.4018/IJIIT.2018100105

APA

Hemalatha, C. S., Sankaran, V., Vaidehi V, Shree Nandhini S, Sharmi P, Lavanya B, Vasuhi S, & Kumar, R. (2018). Symmetric Uncertainty Based Search Space Reduction for Fast Face Recognition. International Journal of Intelligent Information Technologies (IJIIT), 14(4), 77-97. http://doi.org/10.4018/IJIIT.2018100105

Chicago

Hemalatha, C. Sweetlin, et al. "Symmetric Uncertainty Based Search Space Reduction for Fast Face Recognition," International Journal of Intelligent Information Technologies (IJIIT) 14, no.4: 77-97. http://doi.org/10.4018/IJIIT.2018100105

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Abstract

Face recognition from a large video database involves more search time. This article proposes a symmetric uncertainty based search space reduction (SUSSR) methodology that facilitates faster face recognition in video, making it viable for real time surveillance and authentication applications. The proposed methodology employs symmetric uncertainty based feature subset selection to obtain significant features. Further, Fuzzy C-Means clustering is applied to restrict the search to nearest possible cluster, thus speeding up the recognition process. Kullback Leibler's divergence based similarity measure is employed to recognize the query face in video by matching the query frame with that of stored features in the database. The proposed search space reduction methodology is tested upon benchmark video face datasets namely FJU, YouTube celebrities and synthetic datasets namely MIT-Dataset-I and MIT-Dataset-II. Experimental results demonstrate the effectiveness of the proposed methodology with a 10 increase in recognition accuracy and 35 reduction in recognition time.

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