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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 54-60.doi: 10.6040/j.issn.1672-3961.0.2017.420

• 机器学习与数据挖掘 • 上一篇    下一篇

基于多重多元回归的人脸年龄估计

向润1(),陈素芬2,曾雪强3,*()   

  1. 1. 南昌大学信息工程学院，江西 南昌 330031
    2. 南昌工程学院信息工程学院，江西 南昌 330099
    3. 江西师范大学计算机信息工程学院，江西 南昌 330022
  • 收稿日期:2017-08-24 出版日期:2019-04-20 发布日期:2019-04-19
  • 通讯作者: 曾雪强 E-mail:xiangr214@foxmail.com;xqzeng@jxnu.edu.cn
  • 作者简介:向润(1993—)，男，湖北黄冈人，硕士研究生，主要研究方向为机器学习.E-mail：xiangr214@foxmail.com
  • 基金资助:
    国家自然科学基金资助项目(61463033);国家自然科学基金资助项目(61866017);江西省杰出青年人才资助计划资助项目(20171BCB23013);江西省自然科学基金资助项目(20151BAB207028)

Facial age estimation based on multivariate multiple regression

Run XIANG1(),Sufen CHEN2,Xueqiang ZENG3,*()   

  1. 1. Information Engineering School, Nanchang University, Nanchang 330031, Jiangxi, China
    2. School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, Jiangxi, China
    3. School of Computer & Information Engineering, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
  • Received:2017-08-24 Online:2019-04-20 Published:2019-04-19
  • Contact: Xueqiang ZENG E-mail:xiangr214@foxmail.com;xqzeng@jxnu.edu.cn
  • Supported by:
    国家自然科学基金资助项目(61463033);国家自然科学基金资助项目(61866017);江西省杰出青年人才资助计划资助项目(20171BCB23013);江西省自然科学基金资助项目(20151BAB207028)

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摘要：

基于标记分布学习的人脸年龄估计方法利用相近年龄的人脸变化较为缓慢的特点，采用年龄标记分布向量表示附近年龄描述目标年龄的程度，将学习任务从单值的目标年龄预测转变为年龄标记分布向量的估计，较为有效的解决了人脸年龄估计任务中训练数据不足的问题。但是，现有的标记分布学习方法存在不能构建统一的标记分布预测模型(基于最大熵模型的方法)或容易过拟合的问题(基于神经网络的方法)。为了解决这些问题，将基于标记分布学习的年龄估计转换为同时对多因变量进行预测的多重多元回归分析问题，并采用多因变量偏最小二乘回归方法进行求解。多因变量偏最小二乘回归模型对数据分布没有前提假定，在自变量存在较大的相关性的情况下仍可建立有效的多因变量预测模型。在FG-NET人脸数据库上的大量对比试验结果表明，本研究提出的基于多重多元回归的人脸年龄估计方法在大幅度提高模型训练效率的同时，具有更高的年龄估计准确度。

关键词: 人脸年龄估计, 多重多元回归, 偏最小二乘回归, 标记分布学习, 最小二乘回归

Abstract:

Label distribution learning based facial age estimation model was an effective method to solve the problem of insufficient training data caused by the difficulty of facial image collection, where its motivation was that facial aging information on adjacent ages can be introduced to enhance the age estimation model due to human faces changing slowly. Given a certain age to learn, label distribution learning converted the learning target from a continuous value to an age label distribution vector, which was generated according to the description degree of the neighboring ages. However, the existed methods had the drawbacks of separated age prediction model (maximum entropy based methods) or tending to be overfitting (neural network based methods). So a method of facial age estimation based on multivariate multiple regression was proposed, the label distribution learning based age estimation problem was transformed into a multivariate multiple regression analysis task and then solved by the multivariate partial least squares regression. Multivariate partial least squares regression had no assumption about the data distribution and built an integrated effective model for all ages even when there is a strong correlation among independent variables. Extensive comparative experimental results on FG-NET facial age estimation dataset showed that the proposed method significantly improved the training efficiency, and at the same time, had higher age estimation accuracy than the state-of-the-art methods.

Key words: facial age estimation, multivariate multiple regression, partial least squares regression, label distribution learning, least square regression

中图分类号: 

  • TP391

图1

FG-NET数据库中部分人脸图像(每张图片下的数字是其对应的真实年龄)"

图2

PLS-LLD和PLSR在潜在成分数量变化下的MAE性能试验结果"

图3

PLS-LLD和CPNN-LLD在年龄标记高斯分布标准差变化下的MAE性能试验结果"

表1

4种人脸年龄估计方法的MAE和训练时间的试验结果"

方法 MAE 训练时间/s
PLS-LLD 4.55±3.29 1.65±0.54
PLSR 5.75±2.42 0.27±0.43
ⅡS-LLD 6.36±4.15 1481.32±16.87
CPNN-LLD 5.34±3.74 613.65±15.84
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