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Face real-time recognition method and device

A technology of face recognition and recognition method, which is applied in the direction of neural learning method, character and pattern recognition, instrument, etc., can solve the problem that the real-time performance of the face recognition network model is greatly affected, and achieve obvious advantages, reduce the amount of calculation, and improve The effect of face recognition speed

Active Publication Date: 2020-11-03
FENGHUO COMM SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In particular, the deletion of face quality assessment will have a greater impact on the real-time performance of deep face recognition network models

Method used

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  • Face real-time recognition method and device
  • Face real-time recognition method and device
  • Face real-time recognition method and device

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Experimental program
Comparison scheme
Effect test

Embodiment 1

[0046]FaceQnet is a face quality assessment model based on face recognition, which is more robust than the current deep learning for face angle, ambiguity and light condition judgment or single quality feature. Non-face and low-quality face evaluation scores are relatively low, and the accuracy of face quality evaluation is higher than that of other face quality evaluation methods and algorithm models. FaceQnet is a face quality assessment model trained by ISO / ICAO to generate the highest-scoring face image as ground truth, using the highest-scoring face image as a benchmark, and using Facenet face comparison similarity scoring as a label. The biggest advantage of this model is based on the face quality evaluation model trained by face recognition. The evaluated high-quality faces are more suitable for face recognition and can improve the accuracy of face recognition.

[0047] The face detection model yolov3-tiny backbone network is based on darknet19, which is a lightweight n...

Embodiment 2

[0051] Such as figure 2 As shown, the embodiment of the present invention provides a method for real-time face recognition, including:

[0052] S1, the face data training set is obtained after the original data set is evaluated based on the FaceQnet face quality evaluation model;

[0053] Specifically, in order to increase the data volume of the data set and the wide representativeness of the data, the widerface and vggface2 data sets were combined as the original data set to calculate the face pictures selected in the widerface and vggface2 data sets through the FaceQnet face quality assessment model The face quality evaluation score FaceQualityScore is used as the training label of the selected face picture to generate a face data training set. The face data set in the present invention is not limited to widerface and vggface2, and other face detection data sets or data collected and tagged according to actual use scenarios can be used.

[0054] The face quality evaluatio...

Embodiment 3

[0069] Such as image 3 As shown, the embodiment of the present invention provides a real-time face recognition method. When the face category confidence is higher than the preset threshold and the FaceQualityScore is smaller than the preset face quality evaluation score threshold, it is considered that the non-human face is directly detected in the face detection process. throw away.

[0070] For example, in the embodiment of the present invention, two human face quality assessment score thresholds can be set: the first human face quality assessment score threshold and the second human face quality assessment score threshold, wherein the first human face quality assessment score threshold is less than the second human face quality assessment score threshold Evaluation score threshold. First judge whether the confidence of the face category is higher than the preset confidence threshold of the face category and the face quality evaluation score is greater than the first face ...

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Abstract

The invention discloses a face real-time recognition method. The method comprises the steps of evaluating an original data set based on a FaceQnet face quality evaluation model to obtain a face data training set; utilizing the training face data to train an optimized yoov3tiny network model, so as to obtain a trained optimized yoov3tiny network model; detecting to-be-detected pictures by utilizingthe trained optimized yoov3tiny network model to obtain a human face category confidence coefficient and a human face quality evaluation score of each to-be-detected picture; and judging whether to carry out face recognition processing on the to-be-detected picture by utilizing the face category confidence coefficient and the face quality evaluation score of the to-be-detected picture. Accordingto the method, the FaceQnet model and the yoxov3tiny model are combined into one model, the calculation amount of an AI chip is reduced, and therefore the real-time performance of face recognition isfurther improved. The invention further provides a corresponding real-time face recognition device.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and more particularly relates to a method and device for real-time face recognition. Background technique [0002] Embedded face recognition is to use the edge AI chip acceleration engine on the embedded terminal to complete a large number of calculation tasks of the face algorithm, and finally complete the process of face recognition. Embedded face recognition has the advantages of small size, low cost, easy deployment, and convenient distributed computing. [0003] At present, for AI chips with low computing power at the embedded end, in order to reduce the calculation amount of the face algorithm and shorten the calculation time at the embedded end, conventional practice 1: pruning, compressing, and quantizing the model to reduce the model volume, but at the same time resulting in a corresponding decrease in accuracy. Conventional practice two: reduce unnecessary links of face reco...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/161G06V40/172G06N3/045G06F18/214
Inventor 聂建平
Owner FENGHUO COMM SCI & TECH CO LTD
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