Soft-Margin Learning for Multiple Feature-Kernel Combinations With Domain Adaptation, for Recognition in Surveillance Face Dataset

Samik Banerjee, Sukhendu Das; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 169-174

Abstract


Face recognition (FR) is the most preferred mode for biometric-based surveillance, due to its passive nature of detecting subjects, amongst all different types of biometric traits. FR under surveillance scenario does not give satisfactory performance due to low contrast, noise and poor illumination conditions on probes, as compared to the training samples. A state-of-the-art technology, Deep Learning, even fails to perform well in these scenarios. We propose a novel soft-margin based learning method for multiple feature-kernel combinations, followed by feature transformed using Domain Adaptation, which outperforms many recent state-of-the-art techniques, when tested using three real-world surveillance face datasets.

Related Material


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[bibtex]
@InProceedings{Banerjee_2016_CVPR_Workshops,
author = {Banerjee, Samik and Das, Sukhendu},
title = {Soft-Margin Learning for Multiple Feature-Kernel Combinations With Domain Adaptation, for Recognition in Surveillance Face Dataset},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}