Most face recognition networks based on convolutional neural networks are easily affected
by nonlinear factors of expression and posture, and the single task cannot adapt to multi …

At present, there are two main challenges for large-scale face recognition based on deep
learning. One is to design an appropriate loss function to enhance the discrimination ability …

Most face recognition methods rely on deep convolutional neural networks (CNNs) that
construct multiple layers of processing units in a cascaded form and employ convolution …

With the continuous development of deep learning, convolutional neural networks have
achieved good results in the field of face recognition. However, deep convolutional neural …

Face recognition is a method of biometric identification technology that uses a person's face
characteristic data. Face-based characteristics can be easily acquired, unlike fingerprints …

Given that facial features contain a wide range of identification information and cannot be
completely represented by a single feature, the fusion of multiple features is particularly …

Face recognition is an important task in both academia and industry. With the development
of deep convolutional neural networks, many deep face recognition methods have been …

In recent years, pattern recognition has received wide attention, especially face recognition.
Research shows that traditional methods have limits in face recognition under unrestricted …

A face recognition model based on a multiscale feature fusion network is constructed,
aiming to make full use of the characteristics of face and to improve the accuracy of face …

Although face recognition has achieved great success due to deep learning, many factors
may affect the quality of faces in the wild, such as pose changes, age variations, and light …