A Discriminative Metric Learning Algorithm for Face Recognition
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概要
- 論文の詳細を見る
Face recognition is a multi-class classification problem that has long attracted many researchers in the community of image analysis. We consider using the Mahalanobis distance for the task. Classically, the inverse of a covariance matrix has been chosen as the Mahalanobis matrix, a parameter of the Mahalanobis distance. Modern studies often employ machine learning algorithms called metric learning to determine the Mahalanobis matrix so that the distance is more discriminative, although they resort to eigen-decomposition requiring heavy computation. This paper presents a new metric learning algorithm that finds discriminative Mahalanobis matrices efficiently without eigen-decomposition, and shows promising experimental results on real-world face-image datasets.
- 一般社団法人 情報処理学会の論文
著者
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Omachi Shinichiro
Graduate School Of Engineering Tohoku University
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Kato Tsuyoshi
Graduate School Of Frontier Sciences The University Of Tokyo : Aist Computational Biology Research Center
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Takei Wataru
Graduate School of Engineering, Gunma University
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