Improved face key point detection method based on GIOU and weighted NMS

A face detection, face target technology, applied in character and pattern recognition, instruments, biological neural network models, etc., can solve problems such as inability to be directly applied and time-consuming

Active Publication Date: 2019-12-17
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

The traditional face key point detection method is only aimed at the key point detection and recognition of a single face image, resulting in the application of multiple face targets in a comple

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  • Improved face key point detection method based on GIOU and weighted NMS
  • Improved face key point detection method based on GIOU and weighted NMS
  • Improved face key point detection method based on GIOU and weighted NMS

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Embodiment Construction

[0082] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0083]The hardware environment of this experiment is: GPU: Intel Xeon series, memory: 8G, hard disk: 500G mechanical hard disk, independent graphics card: NVIDIA GeForce GTX 1080Ti, 11G; system environment is Ubuntu 16.0.4; software environment is python3.6, OpenCV3 .4.1, caffe. In this paper, the multi-face target detection network is verified on the WIDER FACE dataset. Through actual testing, the recall rate reaches 85.6%. The detection time of a single frame is affected by the number of face targets, which can reach 5-50ms; the face key point detection network Tested on the AFW and LFPW datasets, the error value for single key point detection is within 0.05, and the detection time for a single face target is 20ms. When multiple faces are detected, frame skipping detection is used to ensure the real-time requirements of detection.

[0084] The present inventi...

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Abstract

The invention relates to an improved face key point detection method based on GIOU and weighted NMS. Firstly, a lightweight cascaded face target detection network is adopted to carry out detection androtation angle detection on a face target in an image, GIOU is adopted to replace IOU to serve as a target frame position precision index, and IOU loss is adopted to carry out position regression; secondly, a weighted NMS suppression method is adopted for the obtained face target, and a face target box with high confidence in the image is obtained; and finally, the face key points are detected and regressed by adopting a lightweight cascaded face key point detection network. Compared with a traditional convolutional network, the speed is improved, the real-time performance of the algorithm isenhanced, and the network detection precision is improved on the premise of not additionally increasing the operand. The problem that a traditional target detection and face key point detection method is poor in real-time performance is solved, and real-time key point detection can be carried out on face targets in multiple different rotating directions in a complex environment under the condition that the detection precision is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of computer digital image recognition, and relates to an improved human face key point detection method based on GIoU (Generalized Intersection over Union) and weighted NMS (Convolutional Neural Network), which realizes multi-face target detection and human face detection in a complex environment in one step. Key point detection, which can detect key points of multi-face targets in complex scenes in real time. Background technique [0002] With the continuous advancement of artificial intelligence and computer vision technology, biometric identification technology has gradually entered people's lives. Biometric identification refers to the use of specific biosensor devices to analyze people's inherent physiological and behavioral characteristics, and further model the data to achieve personal identity authentication. The most common biometric technologies include: fingerprint recognition, face recognition, ...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/171G06V40/172G06N3/045
Inventor 李晖晖韩太初郭雷
Owner NORTHWESTERN POLYTECHNICAL UNIV
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