Face age estimation method and system fusing gender and racial information
A race and gender technology, applied in the field of face age estimation methods and systems, can solve problems such as the influence of face age estimation, and achieve the effects of fast training speed, improved accuracy, and fast convergence.
Pending Publication Date: 2022-01-11
CHONGQING UNIV OF POSTS & TELECOMM
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[0004] Although the face age estimation method based on convolutional neural network can better solve the task of age group classification, there are still huge challenges in the accuracy of the age value, and the difference in gender and race also has an impact on face age estimation.
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[0076] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0077] A face age estimation method that integrates gender and race information, such as figure 1 shown, including the following steps:
[0078] Obtain a collection of face images with tags such as age, gender, race, etc., and preprocess the collection of face images;
[0079] Input the preprocessed face image into the convolutional neural network for feature extraction;
[0080] Multi-scale fusion of features extracted from convolutional neura...
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The invention belongs to the field of mode recognition, and particularly relates to a face age estimation method and system fusing gender and racial information, and the method comprises the steps: building a face age prediction model fusing gender and racial information, including feature mapping and employing RepPsconv modules to stack a convolutional neural network, the feature mapping comprises a main module and an auxiliary module. The method comprises the following specific steps: acquiring a face image set with labels such as age, gender, race and the like, and preprocessing the face image set; inputting the preprocessed face image into a convolutional neural network to extract features and fuse the features, wherein the fused features are connected to the main module and the auxiliary module, and the auxiliary module optimizes the result of the main module; carrying out loss solving on an output result of the main module and the human face image label information, and carrying out iterative training until convergence; and inputting a to-be-detected face image into the trained model, and outputting an age estimation result. The invention proposes a high-efficiency RepPSconv module, integrates gender and race information, and improves the precision of face age estimation.
Description
technical field [0001] The invention belongs to the field of pattern recognition, in particular to a method and system for estimating the age of a human face by integrating gender and race information. Background technique [0002] The human face contains many important information related to individual characteristics, which play a key role in people's face-to-face interaction, especially, the biological details contained in the human face, such as wrinkles, freckles, age spots, hair color, face shape, facial Hair, skin texture, etc., can all be used to estimate a person's age. Age estimation through face plays a very important role in social life. [0003] There are two types of methods for face age estimation, one is traditional face age estimation methods based on manual features, which require strong prior knowledge for manual design, and they cannot be relied on to accurately predict human age. The second category is the deep learning method based on convolutional ne...
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IPC IPC(8): G06V10/80G06V40/16G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/047G06N3/045G06F18/25
Inventor 钟福金韩晓乐张序恒
Owner CHONGQING UNIV OF POSTS & TELECOMM