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http://dx.doi.org/10.9717/kmms.2011.14.6.711

A Comparison of Distance Metric Learning Methods for Face Recognition  

Suvdaa, Batsuri (금오공과대학교 컴퓨터공학과)
Ko, Jae-Pil (금오공과대학교 컴퓨터공학과)
Publication Information
Journal of Korea Multimedia Society / v.14, no.6, 2011 , pp. 711-718 More about this Journal
Abstract
The k-Nearest Neighbor classifier that does not require a training phase is appropriate for a variable number of classes problem like face recognition, Recently distance metric learning methods that is trained with a given data set have reported the significant improvement of the kNN classifier. However, the performance of a distance metric learning method is variable for each application, In this paper, we focus on the face recognition and compare the performance of the state-of-the-art distance metric learning methods, Our experimental results on the public face databases demonstrate that the Mahalanobis distance metric based on PCA is still competitive with respect to both performance and time complexity in face recognition.
Keywords
Distance Metric Learning; Mahalanobis Distance; k-Nearest Neighbor Classifier; Face Recognition;
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Times Cited By KSCI : 1  (Citation Analysis)
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