Simple face recognition and classification system

Abstract

The invention discloses a simple face recognition and classification system which comprises a face capture module, a data processing module, a neural network module and a recognition test module. Theface capture module uses an OpenCV or Dlib library to mark and extract a face in the face captured by a camera to obtain a face data set; the data processing module processes the face data set obtained by the face capture module, so that the face data set can be trained and learned by a neural network; the neural network module trains and tests the processed data obtained by the data processing module to obtain a weight model file of network training; and the recognition test module is used for demonstrating face recognition. According to the invention, a TensorFlow deep learning framework israpidly utilized to build a simple face recognition classification system, and more reference possibilities are provided for application of deep learning in face recognition; meanwhile, the system canalso provide more possibilities in the fields of security verification, entrance guard recognition, face payment and the like which apply face recognition.

Description

technical field [0001] The invention relates to a classification system, in particular to a simple face recognition classification system, which provides more reference possibilities for the application of deep learning in face recognition. Background technique [0002] At present, the face recognition technology applying the deep learning framework at home and abroad is relatively mature, and the main research is focused on faster recognition speed and better recognition effect. Most of the relevant technical backgrounds are based on the selection of a certain deep learning framework. , such as TensorFlow, pytorch, paddle paddle and other frameworks, and then use the selected framework to build the network. Most of the network structures currently used are convolutional neural networks, including convolutional layers, pooling layers and fully connected layers. Different recognition systems have different network structures, which will produce different recognition effects. ...

AI Technical Summary

Problems solved by technology

In addition to affecting the final recognition effect on the design of the network, the setting of the data set also has an important impact on the effect of face recognition. At present, most of the practice of completing the framework and applying face recognition is processed through the existing well-known face data sets. , there are not many customizable operations

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