Convolutional neural network face recognition method based on multi-scale pooling

A convolutional neural, multi-scale technology, applied in the field of convolutional neural network face recognition, can solve the problems of loss of important information, destruction of the original image scale and aspect ratio, etc.

Inactive Publication Date: 2016-09-28
ZHEJIANG UNIV
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Problems solved by technology

Therefore, when the size of the input face image is different, it is often necessary to intercept and fix the size of the input image. This artificial change of the size of the input face image destroys the scale and aspect ratio of the original image, which will lead to the loss of some important information. lost

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  • Convolutional neural network face recognition method based on multi-scale pooling
  • Convolutional neural network face recognition method based on multi-scale pooling
  • Convolutional neural network face recognition method based on multi-scale pooling

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Embodiment Construction

[0037] The present invention will be further described below in conjunction with accompanying drawings and implementation examples.

[0038] A kind of face recognition method of convolutional neural network based on multi-scale pooling of the present invention, comprises the following steps:

[0039] (1) Collect standard face grayscale images of 100 people, among which 50 pieces are collected for each person, and 5000 standard face grayscale images are obtained as training images; each training image corresponds to a 100×1-dimensional binary face category label vector y lable =[y 1 the y 2 the y 3 … y t ] T , where the category label vector y of the nth face image lable middle element y i The following conditions should be met:

[0040] y i = 1 i = ...

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Abstract

The invention discloses a convolutional neural network face recognition method based on multi-scale pooling. The method uses the multi-scale pooling-based convolutional neural network for extracting face image features to realize face recognition. During the convolutional neural network building process, a method with alternative convolution and maximum value sampling is adopted for carrying out deep extraction on the features, a multi-scale pooling strategy and a step are adopted for each convolution layer and are together inputted to a fully-connected layer, and thus, characteristic column vectors with multiple scales and a fixed size are provided. Cutting or size adjusting does not need to be carried out on the inputted face image, and images of different sizes can use the same network for training and recognition. According to the convolutional neural network based on the multi-scale pooling, the problem that the size of the input image can be not fixed is solved, the network can extract multi-scale face features, the network performance can be greatly enhanced, and wide application of the multi-scale pooling-based convolutional neural network to face recognition can be promoted.

Description

technical field [0001] The invention belongs to the fields of deep learning and face recognition, and relates to a convolutional neural network face recognition method based on multi-scale pooling. Background technique [0002] Face recognition is a multidisciplinary biometric technology that integrates biology, psychology, and cognitive science. It uses various technologies such as pattern recognition, image processing, and computer vision. Communication and other fields have a wide range of market application prospects. At present, the technical research on face recognition at home and abroad mainly revolves around the two directions of feature extraction and classification algorithms. The face recognition technology based on the deep convolutional neural network is very mature, but the size of the input face image of the traditional convolutional neural network is fixed (for example: 256*256), this is because the BP backpropagation algorithm is used Updating weights and...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/08
CPCG06N3/084G06V40/168G06V40/172
Inventor 刘云海吴斯
Owner ZHEJIANG UNIV
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