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A Face Age Estimation Method Based on Self-paced Learning

A face and normalization technology, applied in the fields of social network entertainment and face age estimation, can solve the problems of poor prediction effect of small data sets, high hardware configuration requirements, etc., to improve learning robustness and powerful representation ability Effect

Active Publication Date: 2022-03-15
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
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AI Technical Summary

Problems solved by technology

The disadvantage is that it requires high hardware configuration requirements, such as GPU servers, etc., and the prediction effect for small data sets is poor

Method used

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  • A Face Age Estimation Method Based on Self-paced Learning
  • A Face Age Estimation Method Based on Self-paced Learning
  • A Face Age Estimation Method Based on Self-paced Learning

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Embodiment Construction

[0122] The present invention is based on the face age estimation method of the depth regression forest of self-step learning, and its realization comprises the following steps:

[0123] Step 1: Preprocess the dataset;

[0124] For Moprh II ( http: / / www.faceaginggroup.com / morph / ) face database uses MTCNN to detect facial feature points, and obtains 5 facial feature points; according to the obtained 5 facial feature point positioning results, the image is normalized to a 224*224*3 RGB image; Processed 55,130 face images with age labels.

[0125] Step 2: Build a deep regression forest;

[0126] image 3 Represents the general structure of the deep regression forest, where the circle represents the feature value output by the last fully connected layer of the convolutional neural network, the square box represents the separation node of each tree, and the diamond box represents the leaf node of each tree;

[0127] The depth regression forest input is the eigenvalue of the la...

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Abstract

The invention discloses a face age estimation method based on self-step learning, which belongs to the fields of computer vision and machine learning. Based on the deep regression forest framework, it divides the face pictures into simple pictures (the absolute error between the predicted age and the actual age is small) and difficult pictures (the absolute error between the predicted age and the actual age is large). Under the self-paced learning framework, the strategy of "from simple pictures to difficult pictures" is adopted to train a deep regression network to establish a nonlinear mapping relationship between facial features and target age. the accuracy and robustness of existing methods. The method can be applied to human-computer interaction, age-based security control, social network entertainment, etc.

Description

technical field [0001] The invention belongs to the technical field of computer vision, relates to face age estimation technology, and is mainly applied to human-computer interaction, age-based security control, social network entertainment and the like. Background technique [0002] Face age estimation technology refers to the technology of automatically estimating the age of the face after analyzing the facial features of the face through computer algorithms. Because this technology can be widely used in human-computer interaction, age-based security control, social network entertainment, etc., it is a hot spot in computer vision and machine learning research in recent years. At present, face age estimation algorithms can be divided into two categories: (1) age estimation algorithms based on shallow models; (2) age estimation algorithms based on deep learning. [0003] The age estimation method based on shallow model is the most common age estimation method, and its basic...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V40/16G06V10/82G06N3/04G06N3/08
CPCG06N3/084G06V40/178G06V40/168G06N3/048G06N3/045
Inventor 艾仕杰程深潘力立
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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