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 and Ranajit Kumar
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C. Sweetlin Hemalatha: VIT University, Vellore, India
Vignesh Sankaran: Madras Institute of Technology, Anna University, Chennai, India
Vaidehi V: VIT University, Vellore, India
Shree Nandhini S: Madras Institute of Technology, Anna University, Chennai, India
Sharmi P: Madras Institute of Technology, Anna University, Chennai, India
Lavanya B: Madras Institute of Technology, Anna University, Chennai, India
Vasuhi S: Madras Institute of Technology, Anna University, Chennai, India
Ranajit Kumar: NCPW, Department of Atomic Energy, Mumbai, India

International Journal of Intelligent Information Technologies (IJIIT), 2018, vol. 14, issue 4, 77-97

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.

Date: 2018
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