Vehicle face recognition method based on deep learning

Abstract

The present invention provides a vehicle face recognition method based on deep learning. The method comprises the steps of: obtaining an image, and constructing a driver data set; constructing a model: extracting visual features and semantic features, and forming a feature processing model; training the feature processing model; optimizing an experiment result according to a test result, and transmitting the optimized feature processing model to a total control terminal; and installing a warning lamp and a camera on a vehicle, and uploading a driver's operation condition to the total control terminal in real time, wherein the total control terminal determines whether the drive breaks driving rules or not according to the optimized feature processing model, when the drive breaks driving rules, the total control terminal emits signals to excite the warning lamp to prompt the driver to perform legal driving. Through real-time analysis of face change, the vehicle face recognition method extracts face features to detect whether there is violation operation or not and perform analysis and comparison with a data set, once behaviors of violating laws and rules are discovered, such as fatigue driving and call answering, watching of a mobile phone, alarm can be automatically emitted to timely stop the drivers' illegal behaviors so as to reduce the possibility of generation of traffic accidents.

Description

technical field [0001] The invention relates to a face recognition method. In particular, it relates to a deep learning-based vehicle face recognition method. Background technique [0002] With the rapid development of information acquisition and information processing technology, computer vision, that is, how to use computer technology to efficiently and accurately obtain relevant information from environmental images or videos, and then analyze, judge and make decisions about things and phenomena in the objective world , has become a very important research topic. With the rapid development of deep learning, computer vision has developed rapidly in recent years, and deep convolutional neural networks have played a very important role in the research and development of computer vision. [0003] With the rapid development of computer vision, the development momentum of face recognition technology is good, and the application of face recognition is becoming more and more ex...

AI Technical Summary

Problems solved by technology

[0005] At present, although my country's vehicle-mounted cameras have developed to a certain extent, they are mainly concentrated in the fields of vehicle speed testing, target tracking, pedestrian detection, and obstacle detection. There is not much research on the driver's safe and civilized driving. There is no real-time and efficient judgment method for judging whether the driver is driving illegally

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