Witness and evidence integration recognition method and system based on deep convolutional neural network

A convolutional neural network and deep convolution technology, which is applied to biological neural network models, neural architectures, instruments, etc., can solve problems such as poor reliability and low recognition rate of human-evidence integration, and achieve improved accuracy and robustness sexual, performance-enhancing effects

Active Publication Date: 2017-05-31
北京品恩科技股份有限公司
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Problems solved by technology

[0007] The purpose of the present invention is to overcome the deficiencies of the prior art, and provide a recognition method and system based on a deep convolutional neural network to solve t

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  • Witness and evidence integration recognition method and system based on deep convolutional neural network
  • Witness and evidence integration recognition method and system based on deep convolutional neural network
  • Witness and evidence integration recognition method and system based on deep convolutional neural network

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Embodiment 1

[0036] This embodiment provides a training method for a two-dimensional face recognition model based on a deep convolutional neural network, such as figure 1 shown, including the following steps:

[0037] Step S1: Use the face image acquisition module to collect face sample images: when collecting face samples, the distance between the face and the camera is 30-60 cm, look directly at the camera, keep a natural expression, and slowly move back and forth, left and right In the process, various expressions and gestures can be revealed. Acquire a face image every 2 seconds, and capture 10 images for each person. The sample images can also be directly replaced by images in the standard face image database.

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Abstract

The invention discloses a witness and evidence integration recognition method and system based on a deep convolutional neural network. By constructing the human face convolution neutral network model, a most difficult distinguishing triple and iterative optimization method is used for training the human face convolutional neural network model, and the deep convolutional neural network model is obtained, the model is used for calculating certificate image and human face image feature values, an Euclidean distance is calculated according to the feature values, and then whether the certificate image is consistent with the human image or not is judged. Compared with the prior art, the method and system has the good robustness for background, illumination, gesture and other changes, constant iteration training can be performed by means of depth learning, the human face recognition performance and the characteristic extraction accuracy are improved, and the system performance is effectively enhanced.

Description

technical field [0001] The present invention relates to the field of biological feature recognition in pattern recognition, in particular to a recognition method and system based on a deep convolutional neural network. 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] Now the second-generation ID card has a built-in non-contact IC smart chip, which stores the holder's face image information and identity information; Identity verification is performed by comparing face images co...

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Application Information

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IPC IPC(8): G07C9/00G06N3/04
CPCG06N3/04G07C9/257
Inventor 俞进森
Owner 北京品恩科技股份有限公司
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