Video-based random gesture authentication method and system

Abstract

The invention discloses a video-based random gesture authentication method. The method comprises the following steps: selecting a registration mode or an authentication mode; collecting a user random gesture video; preprocessing the random gesture video; inputting the preprocessed dynamic gesture video into a random gesture feature extractor, and extracting a feature vector containing physiological features and behavior features of a user; in a registration mode, adding the input user name and the extracted feature vector of the random gesture to a gesture template number database; and in the authentication mode, firstly extracting a plurality of feature vectors corresponding to the user name in the gesture template database, then calculating cosine distances between the feature vectors of the user name and the feature vectors of the user to be authenticated, comparing the minimum cosine distance with a threshold value, and if the minimum cosine distance is lower than the threshold value, determining that the authentication is passed, otherwise, determining that the authentication is not passed. Random gestures are adopted, physiological features and behavior features are combined, and authentication is safer, more efficient and more friendly. The invention further provides a corresponding system.

Description

technical field [0001] The invention belongs to the field of biological feature recognition and video understanding, and more specifically relates to a video-based random gesture authentication method and system in the air. Background technique [0002] Biometric authentication technology is a typical and complex pattern recognition problem, which has been at the forefront of the development of artificial intelligence technology. This technology refers to the science and technology of realizing identity identification by acquiring and analyzing the physiological and behavioral characteristics of the human body. Common biometric modalities include fingerprints, irises, faces, palm prints, hand shapes, veins, handwriting, gait, and voiceprints. After years of development, biometric authentication technology has penetrated into all aspects of people's production and life. From unlocking electronic devices, supermarket cash registers, community access control, to high-speed rai...

AI Technical Summary

Problems solved by technology

The authentication system based on two-stream convolutional neural network uses optical flow to represent behavioral features, which requires twice the amount of parameters and calculations, and the calculation of optical flow is also inefficient.

The authentication system based on the three-dimensional convolutional neural network directly performs spatiotemporal feature modeling through three-dimensional convolution, and extracts behavioral and physiological features at the same time, but the three-dimensional convolution parameters and calculations are also very large

These two systems cannot meet the real-time requirements of actual certified products

It can be seen that the current video-based gesture authentication method still has many deficiencies in the authentication mode and system design, and cannot meet the needs of use.

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