NDLI: Dynamic Batch Size Selection for Batch Mode Active Learning in Biometrics

Content Provider	IEEE Xplore Digital Library
Author	Chakraborty, S. Balasubramanian, V. Panchanathan, S.
Copyright Year	2010
Abstract	Robust biometric recognition is of paramount importance in security and surveillance applications. In face based biometric systems, data is usually collected using a video camera with high frame rate and thus the captured data has high redundancy. Selecting the appropriate instances from this data to update a classification model, is a significant, yet valuable challenge. Active learning methods have gained popularity in identifying the salient and exemplar data instances from superfluous sets. Batch mode active learning schemes attempt to select a batch of samples simultaneously rather than updating the model after selecting every single data point. Existing work on batch mode active learning assume a fixed batch size, which is not a practical assumption in biometric recognition applications. In this paper, we propose a novel framework to dynamically select the batch size using clustering based unsupervised learning techniques. We also present a batch mode active learning strategy specially suited to handle the high redundancy in biometric datasets. The results obtained on the challenging VidTIMIT and MOBIO datasets corroborate the superiority of dynamic batch size selection over static batch size and also certify the potential of the proposed active learning scheme in being used for real world biometric recognition applications.
Starting Page	15
Ending Page	22
File Size	981824
Page Count	8
File Format	PDF
ISBN	9781424492114
DOI	10.1109/ICMLA.2010.10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biometrics active learning Face recognition Clustering algorithms Streaming media Feature extraction DBSCAN clustering Face Discrete cosine transforms numerical optimization
Content Type	Text
Resource Type	Article

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