Compared with the traditional face recognition methods, the deep convolution neural
network model does not need to manually design complex and time-consuming feature …

It is difficult to extract suitable face feature for classification, and the face recognition
accuracy is low under unconstrained condition. To solve the above problems, a new method …

Face recognition technology has attracted much attention in the past few years because of
its potential value of both research and applications in the fields of pattern recognition …

Face identification has been one of the most challenging and attractive areas, and it has
become a popular research area in the computer vision community because of its impact in …

To solve the problem that it is difficult to combine performance and speed of face recognition
in embedded environment, we design and implement a fast face recognition method based …

Face recognition that is technology used for recognizing human faces based on certain
patterns and re-detect faces in various conditions. Face recognition is currently becoming …

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 …

Purpose: Due to the lack of prior knowledge of face images, large illumination changes, and
complex backgrounds, the accuracy of face recognition is low. To address this issue, we …

We develop a more efficient lightweight network based on SE-ShuffleNet V2 to address the
issues of large parameter sizes and sluggish feature extraction rates in large networks in the …

The traditional face recognition technology requires a lot of computation time for human face
feature extraction, and the recognition accuracy is limited by the extracted face features …