基于标记分布学习的人脸年龄识别算法

张会影, 圣文顺, 曾耀徵

江苏大学学报(自然科学版) ›› 2023, Vol. 44 ›› Issue (2) : 180-185.

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江苏大学学报(自然科学版) ›› 2023, Vol. 44 ›› Issue (2) : 180-185. DOI: 10.3969/j.issn.1671-7775.2023.02.008
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基于标记分布学习的人脸年龄识别算法

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Face age recognition algorithm based on label distribution learning

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摘要

针对人脸年龄识别可用数据集普遍不足的问题,为提升可用数据集不变情况下人脸年龄识别的精度,在深度学习(DL)框架中引入标记分布学习(LDL)策略,命名为DL-LDL,其中卷积神经网络用于自动提取人脸特征,改进的标记分布学习用于学习真实年龄及相邻年龄之间的模糊性和多义性,以丰富年龄信息,提高识别精度.将DL-LDL方法在MORPH和FG-NET这2个公开数据集上进行了试验测试.结果表明:DL-LDL方法提高了年龄识别的精度,与现有最先进的人脸年龄识别方法相比,在MORPH和FG-NET上的平均绝对误差分别降低了8.2%和13.8%.

Abstract

To solve the problem that the available datasets for face age recognition were generally insufficient, and improve the accuracy of face age recognition with the same available dataset, the label distribution learning (LDL) strategy was introduced into the deep learning (DL) framework and named as DL-LDL. Convolutional neural networks were used to automatically extract facial features, and the improved label distribution learning was used to learn the fuzziness and ambiguity between the real age and the adjacent ages for enriching the age information and improving the recognition accuracy. The DL-LDL method was tested on two open datasets of MORPH and FG-NET. The results show that the DL-LDL method can improve the accuracy of age recognition. Compared with the most advanced facial age recognition method at present, the mean absolute errors of MORPH and FG-NET are reduced by 8.2% and 13.8%, respectively.

关键词

年龄识别 / 标记分布学习 / 深度学习 / 卷积神经网络 / 特征提取 / 平均绝对误差

Key words

age recognition / label distribution learning / deep learning / convolution neural network / feature extraction / mean absolute error

引用本文

导出引用
张会影, 圣文顺, 曾耀徵. 基于标记分布学习的人脸年龄识别算法[J]. 江苏大学学报(自然科学版), 2023, 44(2): 180-185 https://doi.org/10.3969/j.issn.1671-7775.2023.02.008
ZHANG Huiying, SHENG Wenshun, ZENG Yaozheng. Face age recognition algorithm based on label distribution learning[J]. Journal of Jiangsu University(Natural Science Edition), 2023, 44(2): 180-185 https://doi.org/10.3969/j.issn.1671-7775.2023.02.008

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基金

江苏省高校自然科学研究项目(19KJD520005); 江苏省高校“青蓝工程”项目(苏教师函[2021]11号)

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