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Depressed-angle face recognition method and system based on axial attention weight distribution network

A technology of weight distribution and attention, applied in character and pattern recognition, instruments, computing, etc., to achieve strong discriminative and accurate face recognition

Active Publication Date: 2022-07-22
WUHAN UNIV
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Problems solved by technology

When pedestrians approach the camera, high-resolution images rich in fine texture details can be obtained, but because they are too close to the camera, only face images with a large top-down angle can be obtained

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  • Depressed-angle face recognition method and system based on axial attention weight distribution network
  • Depressed-angle face recognition method and system based on axial attention weight distribution network
  • Depressed-angle face recognition method and system based on axial attention weight distribution network

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Embodiment Construction

[0027] In order to facilitate the understanding and implementation of the present invention by those of ordinary skill in the art, the present invention will be further described in detail below with reference to the accompanying drawings and implementation examples. It should be understood that the implementation examples described herein are only used to illustrate and explain the present invention, but not to limit it this invention.

[0028] see figure 1 , a depression angle face recognition method based on an axial attention weight distribution network provided by the present invention comprises the following steps:

[0029] Step 1: perform feature extraction on the face image with a depression angle to be recognized;

[0030] Collect several face images of different angles and different resolutions, and perform feature extraction on the sequence of face images of different angles and different resolutions to generate a set of normalized feature vectors, which respective...

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Abstract

The invention discloses a depression angle face recognition method and system based on an axial attention weight distribution network. First, CNN is used to extract features from face image sequences of different angles and resolutions to generate a set of normalized feature vectors; Secondly, the cascaded axial attention weight assignment module is used to adaptively assign weights to the image feature sequence, and obtain the weight vector (horizontal) of each feature map itself and the weight vector (vertical) between features to form an axial weight matrix; The weighted aggregation is carried out by using the axial weight to obtain a more discriminative feature vector for identification. The axial matrix weight of the present invention can more delicately represent the weight distribution result between the face sequence images, so the fused features have stronger discrimination, which is beneficial to more accurate depression angle face recognition.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence, and relates to a multi-view face recognition method for surveillance video, in particular to a depression angle face recognition method based on an axial attention weight distribution network. [0002] technical background [0003] As the application of face recognition technology becomes more and more popular, various and complex application scenarios put forward higher requirements for multi-pose face detection and recognition technology. Under the condition of public video surveillance, surveillance cameras are usually installed in a high position to overlook the target, and the captured face image is a depressed face with a certain angle. Although face detection and recognition methods based on deep learning models have become more and more mature, it is still quite difficult to achieve high-precision face detection and recognition from a bird's-eye view. [0004] Since most s...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V40/16G06K9/62G06V10/74
CPCG06F18/22G06F18/253
Inventor 王中元王若溪王南溪李登实
Owner WUHAN UNIV
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