Batch mode active learning for biometric recognition

Shayok Chakraborty; Vineeth Balasubramanian; Sethuraman Panchanathan

doi:10.1117/12.850676

14 April 2010 Batch mode active learning for biometric recognition

Shayok Chakraborty, Vineeth Balasubramanian, Sethuraman Panchanathan

Proceedings Volume 7667, Biometric Technology for Human Identification VII; 76670W (2010) https://doi.org/10.1117/12.850676
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

Active learning methods have gained popularity to reduce human effort in annotating examples in order to train a classifier. When faced with large amounts of data, the active learning algorithm automatically selects appropriate data samples that are most relevant to train the classifier. Typical active learning approaches select one data instance (one face image, for example) in one iteration of the algorithm, and the classifier is trained with the selected data instances, one-by-one. Instead, there have been very recent efforts in active learning to select a batch of examples for labeling at each instant rather than selecting a single example and updating the hypothesis. In this work, a novel batch mode active learning scheme based on numerical optimization of an appropriate function has been applied to the biometric recognition problem. In problems such as face recognition, real-world data is often generated in batches, such as frames of video in a capture session. In such scenarios, selecting the most appropriate data instances from these batches (which usually have a high redundancy) to train a classifier is a significant challenge. In this work, the instance selection is formulated as a mathematical optimization problem and the framework is extended to handle learning from multiple sources of information. The results obtained on the widely used NIST Multiple Biometric Grand Challenge (MBGC) and VidTIMIT biometric datasets corroborate the potential of this method in being used for real-world biometric recognition problems, when there are large amounts of data.

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Shayok Chakraborty, Vineeth Balasubramanian, and Sethuraman Panchanathan "Batch mode active learning for biometric recognition", Proc. SPIE 7667, Biometric Technology for Human Identification VII, 76670W (14 April 2010); https://doi.org/10.1117/12.850676

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KEYWORDS

Biometrics

Optical coherence tomography

Current controlled current source

Diffusion tensor imaging

Facial recognition systems

Information operations

Optimization (mathematics)

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