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

Deep learning models have achieved high performance on many tasks, and thus have been
applied to many security-critical scenarios. For example, deep learning-based face …

Machine learning is enabling a myriad innovations, including new algorithms for cancer
diagnosis and self-driving cars. The broad use of machine learning makes it important to …

Considering various health conditions under varying operational conditions, the mining
sensitive feature from the measured signals is still a great challenge for intelligent fault …

In this paper, we propose a very simple deep learning network for image classification that is
based on very basic data processing components: 1) cascaded principal component …

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 …

Abstract Conceptual Introduction To introduce the concept of cybernetical intelligence, deep
learning, development history, international research, algorithms, and the application of …

Face verification remains a challenging problem in very complex conditions with large
variations such as pose, illumination, expression, and occlusions. This problemis …

This paper targets learning robust image representation for single training sample per
person face recognition. Motivated by the success of deep learning in image representation …