Because a large number of labeled face data samples in special scenes need a large
number of training samples with identity markers, and it is impossible to accurately extract …

As deep residual network has problems such as complex network structure and high time
cost in face recognition applications of small mobile devices, a lightweight model based on …

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 …

In complex environment, the performance of traditional face recognition algorithm decreases
greatly. In order to further improve the recognition accuracy of current face recognition …

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 …

In face recognition, searching a person's face in the whole picture is generally too time‐
consuming to ensure high‐detection accuracy. Objects similar to the human face or multi …

Aiming at the problem of limited discrimination of features extracted by traditional shallow
features and rapid decline of recognition rate in complex environments, a face recognition …

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 …

Purpose In order to solve the problem that the performance of the existing local feature
descriptors in uncontrolled environment is greatly affected by illumination, background …

In order to further improve the ability of automatic feature extraction of deep learning
technology and better solve the problem of system recognition ability declining when the …