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Face Recognition (FR) has been the interest to several researchers over thepast few decades due to its passive nature of biometric authentication. Despitehigh accuracy achieved by face recognition algorithms under controlledconditions, achieving the same performance for face images obtained insurveillance scenarios, is a major hurdle. Some attempts have been made tosuper-resolve the low-resolution face images and improve the contrast, withoutconsiderable degree of success. The proposed technique in this paper tries tocope with the very low resolution and low contrast face images obtained fromsurveillance cameras, for FR under surveillance conditions. For Support VectorMachine classification, the selection of appropriate kernel has been a widelydiscussed issue in the research community. In this paper, we propose a novelkernel selection technique termed as MFKL (Multi-Feature Kernel Learning) toobtain the best feature-kernel pairing. Our proposed technique employs aeffective kernel selection by Multiple Kernel Learning (MKL) method, to choosethe optimal kernel to be used along with unsupervised domain adaptation methodin the Reproducing Kernel Hilbert Space (RKHS), for a solution to the problem.Rigorous experimentation has been performed on three real-world surveillanceface datasets : FR\_SURV, SCface and ChokePoint. Results have been shown usingRank-1 Recognition Accuracy, ROC and CMC measures. Our proposed methodoutperforms all other recent state-of-the-art techniques by a considerablemargin.

Kernel Selection using Multiple Kernel Learning and Domain Adaptation in  Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance  Scenario