Face identification method and attendance system based on deep convolution neural network

Abstract

The invention relates to a face identification method and an attendance system based on a deep convolution neural network. The system comprises that user information and face sample labels are input into a user management server and then sent to a central server; in the central server, the pre-processed sample labels are used to establish a face identification model based on the deep convolution neutral network; employees carry out the online face identification through a trained neutral network on attendance machines at each site, and face identification results will be returned to the user management server by an internal local area network; and management staffs can carry out checking, modification and other operations to attendance records on the user management server. A face identification algorithm based on the deep convolution neutral network used by the invention can avoid problems such as incomplete and uncertain characteristic description caused by traditional manual extraction, can also make use of advantages of a receptive field and weight sharing, and can increase a face identification rate so as to increase an accuracy rate of the attendance system.

Description

technical field [0001] The invention relates to the fields of image processing and pattern recognition, in particular to a face recognition method based on a deep convolutional neural network and an attendance system based on the method. Background technique [0002] Face recognition is mainly used for identification, especially in recent years, with the rapid progress of computer technology, image processing technology, pattern recognition technology, etc., a new biometric recognition technology has emerged. Because it can be widely used in many fields such as security verification, video surveillance, access control, etc., with fast recognition speed and high recognition rate, it has become the main development direction in the field of identification technology research. [0003] The current mainstream face recognition and attendance systems are all based on manual feature extraction, and apply classification algorithms for face recognition. Due to being easily interfere...

AI Technical Summary

Problems solved by technology

Due to being easily interfered by many factors such as light changes, background, posture, etc., when the above external factors change, the manually extracted features will lead to problems such as structure loss, incomplete and uncertain feature descriptions in the original image, etc. These defects lead to artificial The face recognition rate is low, the reliability is poor, and it cannot be promoted in a large area, etc.

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