The goal of this paper is face recognition–from either a single photograph or from a set of
faces tracked in a video. Recent progress in this area has been due to two factors:(i) end to …

This paper designs a high-performance deep convolutional network (DeepID2+) for face
recognition. It is learned with the identification-verification supervisory signal. By increasing …

Convolutional neural networks (CNNs) have been widely used in computer vision
community, significantly improving the state-of-the-art. In most of the available CNNs, the …

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 Neural Networks (DNNs) have established themselves as a dominant technique in
machine learning. DNNs have been top performers on a wide variety of tasks including …

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising
results in face recognition recently. However, it remains an open question: why CNNs work …

PCANet was proposed as a lightweight deep learning network that mainly leverages
Principal Component Analysis (PCA) to learn multistage filter banks followed by binarization …

Face recognition (FR) methods report significant performance by adopting the convolutional
neural network (CNN) based learning methods. Although CNNs are mostly trained by …

We present a hybrid neural-network for human face recognition which compares favourably
with other methods. The system combines local image sampling, a self-organizing map …

Scaling machine learning methods to very large datasets has attracted considerable
attention in recent years, thanks to easy access to ubiquitous sensing and data from the …