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Face recognition method based on feature analysis

A face recognition and feature analysis technology, applied in the field of face recognition, can solve problems such as poor robustness, achieve high accuracy, good robustness, and solve the effects of uneven illumination

Inactive Publication Date: 2018-11-27
CHENGDU KOALA URAN TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a face recognition method based on feature analysis, which solves the technical problem that the current face recognition algorithm has poor robustness in actual open set evaluation and recognition and is not suitable for use in industry

Method used

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  • Face recognition method based on feature analysis

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Experimental program
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specific Embodiment 1

[0085] A face recognition method based on feature analysis, comprising the following steps (such as figure 1 shown):

[0086] Step 1: Construct a neural network model for extracting features, use the face data set to train, and obtain a trained neural network model; the neural network model is composed of an Inception module and a ResNet module, and the neural network composed of these two modules The network model is an existing scheme;

[0087] When training the neural network model, the loss function used is:

[0088]

[0089] Among them, m represents the batch size (batchsize), i represents the sample label, and x i Indicates the i-th sample, y i Represents the label of the i-th sample, T represents the transpose operation, j represents the label label, n represents the total number of categories, and λ represents the hyperparameter, which is used to control The weight of , after experiments, the effect is best when λ is 0.01;

[0090] c represents the class cente...

specific Embodiment 2

[0124] Based on Embodiment 1, this embodiment provides a welcome and access control system.

[0125] The system (such as figure 2 Shown) includes a camera arranged at the entrance, the camera is connected to the Ethernet through a local area network, the switch at the entrance to control the opening and closing of the entrance is connected to the Ethernet, and the face recognition server, storage server and display are all connected to the Ethernet.

[0126] The workflow of this system is:

[0127] Step 1: The face recognition system loads the trained neural network model to extract image features;

[0128] Step 2: The face recognition system registers the people who need to use the system for verification (such as all the staff in a building), that is, use the trained neural network model to perform feature extraction on the face images of all the people who need to be verified , build a face feature library;

[0129] Step 3: The camera collects the face image at the entr...

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Abstract

The invention discloses a face recognition method based on feature analysis, and belongs to the field of face recognition. A neural network model for feature extraction is constructed and trained by using a face data set so as to obtain the trained neural network model; constructing a face feature library; the face image to be recognized is filtered and the processed image is inputted into the trained neural network model to extract the features; and the similarity between the extracted features and the features in the face feature library is calculated, if the similarity is greater than the threshold, the linear discriminant is applied to improve the matching accuracy of the features of which the similarity is greater than the threshold and the recognition result is obtained, or the faceimage corresponding to the feature is discarded. The face recognition method is suitable for multiple actual environments such as the access control system, the bank system, etc. Compared with the mainstream face recognition SDK, the system algorithm has good robustness to the environment change.

Description

technical field [0001] The invention relates to the field of face recognition, in particular to a face recognition method based on feature analysis. Background technique [0002] In recent years, we have witnessed the great success of Convolutional Neural Network (CNNs) algorithms in Face Recognition (FR). Thanks to advanced deep network architectures such as DCNN, ResNet, GoogleNet and discriminative learning methods, deep CNNs improve the performance of FR to an unprecedented level. In general, face recognition can be classified into face recognition and face verification. The former approach is geared toward classifying specific identities, while the latter decides whether a pair of faces belong to the same identity. [0003] In terms of testing methods, face recognition can be evaluated in closed-set or open-set settings. For closed-set evaluation, all test identities are predefined in the training set. Naturally, face images can be classified into the given Identity,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/16G06V40/168G06F18/2132G06F18/22G06F18/214
Inventor 麦智钧沈复民
Owner CHENGDU KOALA URAN TECH CO LTD
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