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Domain Adaptation with Soft-margin multiple feature-kernel learning beats Deep Learning for surveillance face recognition
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.
Kernel Selection using Multiple Kernel Learning and Domain Adaptation in Reproducing Kernel Hilbert Space, for Face Recognition under Surveillance Scenario
The proposed technique in this paper tries to cope with the very low resolution and low contrast face images obtained from surveillance cameras, for FR under surveillance conditions.
SD-GAN: Structural and Denoising GAN reveals facial parts under occlusion
Certain facial parts are salient (unique) in appearance, which substantially contribute to the holistic recognition of a subject.
Detection and skeletonization of single neurons and tracer injections using topological methods
Neuroscientific data analysis has traditionally relied on linear algebra and stochastic process theory.
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