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Age estimation method for face image

A face image and input image technology, applied in the field of face age estimation, can solve the problem of too many model parameters, and achieve the effect of solving too many model parameters, high-precision age estimation, and fast learning

Active Publication Date: 2016-09-21
HUAQIAO UNIVERSITY
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Problems solved by technology

[0004] The main purpose of the present invention is to overcome the above-mentioned defects in the prior art, and propose a method for estimating the age of a face image based on a deep learning DLPCANet model. Multiple questions, and achieve fast learning and high-precision age estimation

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Embodiment Construction

[0034] The present invention will be further described below through specific embodiments.

[0035] exist figure 1 Among them, it can be seen that the present invention is an age estimation method based on the deep learning model DLPCANet, which includes three layers, namely the convolutional layer (The Convolutional Layer), the nonlinear layer (The Nonlinear Process Layer), and the feature extraction layer (Feature Pooling Layer).

[0036] refer to Figure 1 to Figure 7 , the age estimation method of a kind of face image of the present invention, comprises the steps:

[0037] 1) Preprocess the input face image, including binarization, smoothing and normalization, to obtain a grayscale image with a size of m×n pixels, and divide all the obtained grayscale images into blocks, such as figure 2 As shown, assuming that the size of each block is p 1 ×p 2 , the step in the block process is s 1 =s 2 = 1, the block process can also be regarded as using a window size s 1 =s 2...

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Abstract

The invention relates to an age estimation method for a face image. The age estimation method comprises the following five parts: (1) an image is blocked; (2) a matrix after blocking processing is learnt by using a PCA algorithm so as to acquire a convolution kernel for a convolution operation; (3) the acquired convolution kernel is learnt by using the PCA algorithm so as to carry out the convolution operation; (4) nonlinear processing is carried out behind a second convolution layer by using a mode of binarization; and (5) feature extraction is carried out by using a method of histogram statistics. According to the method, an age value is estimated by using nonlinear K-SVR (Kernel function Support Vector Regression) after feature extraction, and experiments show that the accuracy of age estimation can be greatly improved.

Description

technical field [0001] The invention relates to the field of face age estimation, in particular to a method for age estimation of a face image based on a deep learning model. Background technique [0002] In the face age automatic estimation system, it is usually divided into two stages. The first stage is to extract age features, and the second stage is to estimate age. Usually, the focus of research is how to extract the best age features. In the existing age estimation systems, the methods of learning or extracting age features can generally be divided into manual extraction of age features and automatic learning of age features. Representative methods for manually extracting age features include LBP (Local Binary Patterns, LBP), SIFT (Scale-Invariant Feature Transform, SIFT), subspace models, etc. The disadvantage of manual feature extraction is that it is affected by human subjective selection. Although manual selection of features may achieve very good results in some...

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/178G06V40/16G06V40/168
Inventor 杜吉祥郑德鹏翟传敏范文涛王靖刘海建
Owner HUAQIAO UNIVERSITY
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