A system that adjusts the sitting posture of people in the vehicle based on the identified person information

Abstract

A system for adjusting the sitting posture of a person in a vehicle according to the identified personnel information, which includes a sensor activation unit, a memory unit, an authentication system unit, a seat adjustment unit, etc.; the sensor activation unit is used to detect whether there is a person on the seat in the vehicle; The memory unit is used to store a plurality of pre-stored vehicle personnel information; the authentication system unit includes a neural network unit. In the prior art, for the training of the neural network, the large training set and the limitation of video memory have become the main contradiction in the training of the face recognition model. The invention provides a training system for a face recognition model, including a data input layer, a feature fusion layer, a classifier and a loss function; different classifiers classify different contents of the sample images, effectively solving the problem of training people In the face recognition model, it is impossible to train a large-scale face dataset due to memory limitations.

Description

Technical field [0001] The present invention relates to the field of facial recognition, and more particularly to personnel face recognition in a vehicle driving environment, and adjust the person in the vehicle by identifying personnel information. Background technique [0002] Face recognition: a biometric identification technology identified based on human face feature information. It is widely used in all aspects of human life. In the field of vehicle driving, because of the gender, height, weight, etc. of each person, is not a seat adjustment location of driving or passenger seats for everyone. Therefore, people can only adjust the seat posture by some of the buttons such as the vehicle, including the seat, the height of the rear backrest, the front-rear position of the seat, the tilt angle of the rear backrest, etc., although the existing button, etc. The human machine is friendly and friendly, and there is no need to use too much strength, and even integrate the adjustment...

AI Technical Summary

Problems solved by technology

Although this method greatly simplifies the adjustment steps, it also has disadvantages: 1. The number of personnel that can be set is limited, and often only a few groups of personnel information can be memorized; when there are other personnel on the vehicle who have not set parameter groups, they cannot Automatically complete the adjustment; 2) It still requires personnel to select the parameter group option that suits them, so the inconvenience of adjustment has not been fundamentally changed

[0004] like figure 1 As shown, in the traditional method, each graphics card shares the same data input and the same classifier; generally in the industry, a commercially available face recognition model is often based on a training set of millions or even tens of millions of people. However, generally on a graphics card with a single video memory of 12G, when the dimension of the feature vector is 512 dimensions, the number of classifiers is generally at most about 300,000, otherwise it will face problems such as video memory overflow

Large training sets and video memory limitations have become the main contradiction in face recognition model training

Images