A Face-Gesture Cooperation Verification Method Based on Deep Learning Detection

Abstract

A face-gesture cooperation verification method based on deep learning detection, the main purpose of which is to use an algorithm suitable for low-power devices to complete the detection of faces and gestures, so as to achieve the effect of face-gesture cooperation verification. It is a kind of overall detection of head and shoulders in the greatly reduced image using deep residual convolutional neural network, and then detects the face according to the range of the head and shoulders, and finally detects the gesture according to the position of the head and shoulders, and according to the detected person Methods for face and gesture verification triggers. Compared with the traditional gesture-based verification system, the present invention has better stability, greatly reduces user false triggers and system failures, and has good stability. The method can be widely applied to the method for verifying the power on and off of mobile phones and home appliances.

Description

technical field [0001] The present invention relates to technical fields such as artificial neural network and computer vision, and in particular to a method for verifying human face gesture cooperation based on deep learning detection. Background technique [0002] Static gesture recognition has important applications in visual communication, human-computer interaction, augmented reality and other fields. However, in practical applications, due to factors such as illumination and individual changes, it is difficult for simple gesture recognition to meet specific application requirements. In recent years, in the home appliance industry such as televisions, air conditioners, and air purifiers, the traditional method of relying on remote controls has become more and more clumsy, while the control method of relying on face plus gesture verification has become more and more popular in the high-end home appliance industry and the smart phone industry. Pay attention to. The face...

AI Technical Summary

Problems solved by technology

The current mainstream target detection algorithms such as SSD, R-FCN, mask R-CNN, etc. use popular deep network structures such as VGG-16, Resnet101, etc., and the time required to run it once using an ordinary notebook CPU cannot meet the real-time requirements. Devices with no GPU power consumption will cause memory overflow problems, which are far from meeting the requirements of low-power devices in the home appliance and mobile phone industries.

Furthermore, the low-power chips of these devices often run other programs at the same time in actual use, and have strict requirements on heat generation. Therefore, it is difficult to implement and implement common deep learning algorithms on these devices.

Images