A practical deep learning face recognition system can be divided into several tasks. These
tasks can be time‐consuming if each task is executed with the original image as the input …

Face recognition is difficult due to the higher dimension of face image features and fewer
training samples. Firstly, in order to improve the performance of feature extraction, we …

Face recognition (FR) is one of the most popular and long-standing topics in computer
vision. With the recent development of deep learning techniques and large-scale datasets …

Most facial recognition and face analysis systems start with facial detection. Early
techniques, such as Haar cascades and histograms of directed gradients, mainly rely on …

Face detection is crucial in the development of face recognition, expression, tracking, and
classification. Conventional methods have accuracy constraints on several challenging …

Recently, significant improvements on face detection have been achieved by convolutional
neural networks. However, the speed of a CNN-based face detector is hampered by the …

With the application of deep convolutional neural networks, the performance of computer
vision tasks has been improved to a new level. The construction of a deeper and more …

Plenty of face detection and recognition methods have been proposed and got delightful
results in decades. Common face recognition pipeline consists of: 1) face detection, 2) face …

Lightweight convolutional neural networks are widely used for face detection due to their
ability to learn local representations through spatial induction bias and translational …

Abstract The Convolutional Neural Network (CNN) based on deep learning is introduced to
propose two deep face detection algorithms and design an embedded face recognition …