A face attribute analysis method based on transfer learning

A technology of transfer learning and attribute analysis, which is applied in the direction of instruments, biological neural network models, character and pattern recognition, etc., to achieve the effect of preventing local minima, avoiding accuracy reduction, and improving prediction accuracy

Active Publication Date: 2019-02-12
SOUTHEAST UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a face attribute analysis method based on transfer learning, which realizes more flexible and accurate face attribute analysis, and solves the problem that traditional attribute analysis only uses simple classification tasks. The technical problem of overfitting

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  • A face attribute analysis method based on transfer learning
  • A face attribute analysis method based on transfer learning
  • A face attribute analysis method based on transfer learning

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Embodiment Construction

[0022] The technical solution of the invention will be described in detail below in conjunction with the accompanying drawings.

[0023] In view of the fact that the traditional face attribute analysis method is too simple, it is easy to fall into the problem of overfitting during training, and proposes a multi-task training that integrates multiple complex face attributes such as face bounding boxes.

[0024] Face attributes include various face-related linear regression and logistic regression tasks. Face attributes based on logistic regression include face judgment and facial features. Face attributes based on linear regression include the relative positions of facial features on the face. , the relative position of the face frame in the whole picture, etc.

[0025] The face attribute analysis method based on migration learning proposed by the present invention is as follows: figure 1 As shown, it mainly includes the following four major steps.

[0026] Step 1: Design the...

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Abstract

The invention discloses a face attribute analysis method based on transfer learning, belonging to the technical field of calculation and calculation, in particular to the technical field of computer vision for recognizing the face attribute. The invention combines training sample sets on a multi-attribute prediction network to predict feature attributes, migrating the convergent multi-attribute prediction network to the master attribute prediction network, the master attribute prediction network is trained and the parameters are fine-tuned until the loss function of the master attribute prediction network converges, The main attributes include but are not limited to the face attributes based on logical regression and the main attributes based on linear regression, which not only prevents the local minima, but also avoids the precision degradation caused by the excessive complexity of tasks, and is more accurate and flexible in practical application.

Description

technical field [0001] The invention discloses a human face attribute analysis method based on transfer learning, which belongs to the technical field of calculation and calculation, and in particular relates to the computer vision technical field of identifying human face attributes. Background technique [0002] Face attribute analysis refers to analyzing whether a specific picture is a human face, correcting a face that is not in the center of the image or is too large or too small, locating the key points of the face, and judging the facial features of the face. The different attributes analyzed can be applied to different occasions: judging whether it is a human face can filter out non-human faces that are falsely detected in face detection; correcting faces that are not in the center of the image or are too large or too small, and locating key points of the face can fine-tune the face The result of the detection; distinguishing facial features can further provide a fea...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/168G06V40/172G06N3/045G06F18/214
Inventor 陆生礼庞伟向家淇周世豪杨文韬泮雯雯
Owner SOUTHEAST UNIV
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