The capacity to recognize faces under varied poses is a fundamental human ability that
presents a unique challenge for computer vision systems. Compared to frontal face …

Deep learning, in particular the deep convolutional neural networks, has received
increasing interests in face recognition recently, and a number of deep learning methods …

Human faces in surveillance videos often suffer from severe image blur, dramatic pose
variations, and occlusion. In this paper, we propose a comprehensive framework based on …

Abstract In 2007, Labeled Faces in the Wild was released in an effort to spur research in
face recognition, specifically for the problem of face verification with unconstrained images …

Feature extraction is vital for face recognition. In this paper, we focus on the general feature
extraction framework for robust face recognition. We collect about 300 papers regarding face …

This paper learns a graphical model, namely an explanatory graph, which reveals the
knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv …

In this paper, we propose a simultaneous local binary feature learning and encoding
(SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike …

Deep neural networks (DNNs) trained on large-scale datasets have recently achieved
impressive improvements in face recognition. But a persistent challenge remains to develop …

Deep learning is well known as a method to extract hierarchical representations of data. In
this paper a novel unsupervised deep learning based methodology, named Local Binary …

Pain is a symptom of many disorders associated with actual or potential tissue damage in
human body. Managing pain is not only a duty but also highly cost prone. The most primitive …