TY - GEN
T1 - Dynamic batch size selection for batch mode active learning in biometrics
AU - Chakraborty, Shayok
AU - Balasubramanian, Vineeth
AU - Panchanathan, Sethuraman
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Active learning
KW - DBSCAN clustering
KW - Numerical optimization
UR - http://www.scopus.com/inward/record.url?scp=79952411828&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952411828&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2010.10
DO - 10.1109/ICMLA.2010.10
M3 - Conference contribution
AN - SCOPUS:79952411828
SN - 9780769543000
T3 - Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
SP - 15
EP - 22
BT - Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010
T2 - 9th International Conference on Machine Learning and Applications, ICMLA 2010
Y2 - 12 December 2010 through 14 December 2010
ER -