Face attribute recognition method based on multi-task deep learning

A deep learning and attribute recognition technology, applied in the field of face attribute recognition, can solve the problems of time-consuming and labor-intensive acquisition of label data, insufficient data, etc.

Active Publication Date: 2016-12-07
XIAMEN UNIV
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AI Technical Summary

Problems solved by technology

However, obtaining a large amount of labeled data is often very time-consuming and laborious. It is a key problem worth solving to explore

Method used

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  • Face attribute recognition method based on multi-task deep learning
  • Face attribute recognition method based on multi-task deep learning
  • Face attribute recognition method based on multi-task deep learning

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Embodiment Construction

[0051] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0052] The present invention comprises the following steps:

[0053] S1. Prepare an image data set, which contains a large number of faces and corresponding face attribute labels. The face attribute database used in this example is the image dataset in the CelebrayA database, which contains more than 200,000 face images and 40 face attributes. Three representative face attribute tasks (K=3): face gender attribute, face smile attribute and face attractiveness attribute are used for illustration. The schematic diagram of the three properties is as follows figure 1 with 2 As shown, the label is set to y respectively 1 ,y 2 ,y 3 .

[0054] S2. Perform face detection on each image in the image data set one by one, and obtain the position of the face in each image. In this step, any existing face detection method may be used for face d...

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Abstract

The invention provides a face attribute recognition method based on the multi-task deep learning and relates to the face attribute recognition technique in the field of computer vision. The method comprises the steps of preparing an image data set; subjecting each image in the image data set to face detection one by one; detecting face key points in all detected faces; aligning each face with a standard face image according to the face alignment method based on detected face key points to form a face image training set; calculating an average face image in the training set; constructing a multi-task deep convolutional neural network, and training network parameters after subtracting the average face image from each face image in the face image training set so as to obtain a convolutional neural network model; detecting faces and face key points in a to-be-recognized test image, and aligning each face in the above image with the standard face image based on the face key points; placing the standard face image into the constructed convolutional neural network model after subtracting the average face image from the standard face image and conducting the feedforward arithmetic operation so as to obtain a result.

Description

technical field [0001] The invention relates to face attribute recognition in computer vision, in particular to a face attribute recognition method based on multi-task deep learning. Background technique [0002] The image-based face attribute recognition method is a process of judging the face attributes in the image by using pattern recognition technology according to a given input image. The face attributes contained in the face image mainly include: age, gender, expression, race, whether to wear glasses, whether to make up, etc. Using computer to automatically recognize face attributes can effectively improve the performance of human-computer interaction and has very important practical application value. The process of face attribute recognition includes: face detection technology, face image preprocessing technology, face feature extraction, face attribute classifier training and other steps. Among them, the performance of face feature extraction and face attribute c...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/161G06V40/171G06V40/172G06F18/214G06F18/24
Inventor 严严陈日伟王菡子
Owner XIAMEN UNIV
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