  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.
