BP (back propagation) neural network-based momentum face detection method

A BP neural network and face detection technology, which is applied in the field of momentum face detection based on BP neural network, can solve the problems of easy oscillation and long convergence time of neural network, and achieve the effect of improving rapidity and slowing down the trend of oscillation

Inactive Publication Date: 2017-12-29
HUZHOU TEACHERS COLLEGE
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is: to design a method to solve the problem that

Method used

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  • BP (back propagation) neural network-based momentum face detection method
  • BP (back propagation) neural network-based momentum face detection method
  • BP (back propagation) neural network-based momentum face detection method

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Embodiment Construction

[0022] The present invention first selects Gabor kernels on five scales and eight directions to extract the Gabor features in the input image, and convolves the input image with 5*8 Gabor kernels, as Figure 5 As shown, these Gabor kernels are used to generate 40 image features of different scales at different frequencies, which can also be called 40 Gabor filters. The rows in the figure represent eight different orientations, and the columns represent five different scales. The specific process is as follows:

[0023] Given an input image as the corresponding input signal f in , first use Fourier transform to transform it into the frequency domain

[0024]

[0025] (x, y) represent coordinates on the spatial domain. The result of the spatial signal is then multiplied by a Fourier transform of a Gabor kernel that can obtain the resulting image filtered by a Gabor filter.

[0026] Using the convolution theorem formula (2) is as follows,

[0027]

[0028] Among them...

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Abstract

The invention relates to a BP (back propagation) neural network-based momentum face detection method. According to the method, Gabor features and a momentum factor back propagation algorithm are combined. The method includes the following steps that: the Gabor features of training sets are extracted; the Gabor features are inputted into a momentum factor back propagation neural network so as to perform training; and the trained system is adopted to detect whether a face exists in an inputted image, and a face exists in the inputted image, the face is marked by a rectangle. In order to improve the training effect of a traditional back propagation algorithm, the momentum factor is added to the algorithm, and therefore, the vibration trend of the neural network in training can be effectively slowed down, and the algorithm can be prevented from falling into the local minimum; and the added momentum factor can adaptively adjust the weight of each layer of the back propagation neural network. Numerous experimental results show that, compared with classical or the most advanced face detection models, the BP (back propagation) neural network-based momentum face detection method of the invention is effective and competitive.

Description

technical field [0001] The invention relates to the field of image data processing, in particular to a momentum face detection method based on BP neural network. Background technique [0002] Face detection is a worthy research topic in computer vision. In the past many years, researchers have invested a lot of experience in the field of face detection. The essence of face detection is to mark out the faces in the image with rectangular labels. With the increase of face detection applications, face detection has gradually developed into an independent research topic, which has attracted the attention of researchers. [0003] Generally, face detection can be divided into two categories: one is face detection on static images (grayscale or color images), and single or multiple faces can be detected on the image according to the number of faces on the image . The other is face detection on dynamic images, also known as target tracking. The research in this paper is based o...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/08
CPCG06N3/084G06V40/161G06V10/449G06F18/214
Inventor 蒋林华蒋云良曹书慧林晓胡文军龙伟
Owner HUZHOU TEACHERS COLLEGE
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