A Mini-Batch Discriminative Feature Weighting Algorithm for LBP - Based Face Recognition

Proceedings of IEEE International Conference on Imaging Systems and Techniques (IST 2012 ) 2012
Oļegs Ņikišins, Modris Greitāns

This paper proposes a mini-batch discriminative feature weighting methodology for minimization of classification error in datasets with considerable number of classes and poor intra class information. Presented approach improves the classification system by enhancing the components more relevant to the recognition. It is based on the maximization of interclass Euclidean distance by utilization of information from all classes. A weighted nearest neighbor classifier is used for the classification. A mini-batch principle is implemented into the training process in order to boost the learning speed, which is a bottleneck for traditional batch algorithms. We report how the weighting can be applied to the task of Local Binary Patterns-based face recognition. The performance of the algorithm is evaluated on a color FERET database.

Keywords
face recognition , feature extraction , image classification , learning (artificial intelligence) , visual databases
DOI
10.1109/IST.2012.6295521
Hyperlink
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6295521

Ņikišins, O., Greitāns, M. A Mini-Batch Discriminative Feature Weighting Algorithm for LBP - Based Face Recognition. In: Proceedings of IEEE International Conference on Imaging Systems and Techniques (IST 2012 ), United Kingdom, Manchester, 16-17 July, 2011. Piscataway: IEEE, 2012, pp.170-175. ISBN 978-1-4577-1776-5. Available from: doi:10.1109/IST.2012.6295521

Publication language
English (en)