Face recognition method for combined original data and generated data

A primitive face technology, applied in the field of training face recognition neural network, can solve the problems of many network parameters, large manpower and financial resources, consumption, etc.

Active Publication Date: 2018-06-05
中科汇通投资控股有限公司
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Problems solved by technology

[0003] However, the biggest problem with deep learning methods is that there are too many network parameters, and large-scale data labeling is required to achieve training, often requiring more than one mill

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  • Face recognition method for combined original data and generated data
  • Face recognition method for combined original data and generated data
  • Face recognition method for combined original data and generated data

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Embodiment Construction

[0019] The objects and functions of the present invention and methods for achieving the objects and functions will be clarified by referring to the exemplary embodiments. However, the present invention is not limited to the exemplary embodiments disclosed below; it can be implemented in various forms. The essence of the description is only to help those skilled in the relevant art comprehensively understand the specific details of the present invention.

[0020] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar components, or the same or similar steps.

[0021] Step 101, training a convolutional neural network VGG face recognition model. The face recognition network of the present invention adopts the VGGface network, adopts softmax loss and triplet loss loss function during training, and adopts the face sample set S 0 train.

[0022] St...

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Abstract

The invention provides a method for training a convolution neural network through a small-scale face data set. The method is characterized by comprising the following steps: step one, using an originally marked face sample set to train a VGG face recognition model of the convolution neural network; step two, constructing a deep convolution to generate a DCGAN model of a confrontation network, andusing the originally marked face sample set to train the deep convolution so as to generate the confrontation network; step three, generating an unlabelled face sample set through DCGAN; step four, generating a face data set mark for the DCGAN; step five, using the originally marked face sample set to train a plug-and-play generated network PPGN; step six, generating a labeled face sample set through the PPGN; step seven, training the convolution neural network in combination with a DCGAN and PPGN generated sample set and the originally marked sample set; step eight, repeatedly training, namely repeating the step four, step five, step six and step seven repeatedly; and step nine, using the originally marked face sample set to finely adjust the VGG network.

Description

technical field [0001] The invention relates to the field of biological feature recognition, in particular to a method for training a face recognition neural network by simultaneously utilizing original label data and data generated by a generative confrontation network. Background technique [0002] Face recognition is a technology for identification based on human facial feature information. Because of its naturalness, non-mandatory and non-contact advantages, it has become a popular research field in computer vision. The key technology of face recognition is to effectively express the features of the face image, while the traditional manual selection of features such as SIFT and HOG is not enough to capture the essential features of the face. In recent years, the deep learning method has been successfully applied to the field of face recognition. By building a deep neural network to fit the face image, the essential expression of the face image features can be obtained, a...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/16G06N3/045
Inventor 许浩
Owner 中科汇通投资控股有限公司
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