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Intra-Class Threshold Selection in Face Space Using Set Estimation Technique

Intra-Class Threshold Selection in Face Space Using Set Estimation Technique

Madhura Datta, C. A. Murthy
Copyright: © 2011 |Pages: 16
ISBN13: 9781616927974|ISBN10: 1616927976|EISBN13: 9781616927998
DOI: 10.4018/978-1-61692-797-4.ch004
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MLA

Datta, Madhura, and C. A. Murthy. "Intra-Class Threshold Selection in Face Space Using Set Estimation Technique." Kansei Engineering and Soft Computing: Theory and Practice, edited by Ying Dai, et al., IGI Global, 2011, pp. 69-84. https://doi.org/10.4018/978-1-61692-797-4.ch004

APA

Datta, M. & Murthy, C. A. (2011). Intra-Class Threshold Selection in Face Space Using Set Estimation Technique. In Y. Dai, B. Chakraborty, & M. Shi (Eds.), Kansei Engineering and Soft Computing: Theory and Practice (pp. 69-84). IGI Global. https://doi.org/10.4018/978-1-61692-797-4.ch004

Chicago

Datta, Madhura, and C. A. Murthy. "Intra-Class Threshold Selection in Face Space Using Set Estimation Technique." In Kansei Engineering and Soft Computing: Theory and Practice, edited by Ying Dai, Basabi Chakraborty, and Minghui Shi, 69-84. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-61692-797-4.ch004

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Abstract

Most of the conventional face recognition algorithms are dissimilarity based, and for the sake of open and closed set classification one needs to put a proper threshold on the dissimilarity value. On the basis of the decision threshold, a biometric recognition system should be in a position to accept the query image as client or reject him as imposter. However, the selection of proper threshold of a given class in a dataset is an open question, as it is related to the difficulty levels dictated in face recognition problems. In this chapter, the authors have introduced a novel thresholding technique for a real life scenario where the query face image may not be present in the training database, i.e. often referred by the biometric researchers as the open test identification. The theoretical basis of the thresholding technique and its corresponding verification on several datasets has been successfully demonstrated in the article. The proposed threshold selection is based on statistical method of set estimation and is guided by minimal spanning tree. It has been found that the proposed technique performs better than the ROC curve based threshold selection mechanism.

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