计算机科学技术学报 ›› 2021,Vol. 36 ›› Issue (3): 508-519.doi: 10.1007/s11390-021-1325-9

所属专题： Computer Graphics and Multimedia

• • 上一篇    下一篇

婴儿的年龄和性别容易被识别吗？

Yang Liu, Ruili He, Xiaoqian Lv, Wei Wang, Xin Sun, and Shengping Zhang*   

  1. School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, China
  • 收稿日期:2021-01-27 修回日期:2021-04-25 出版日期:2021-05-05 发布日期:2021-05-31
  • 通讯作者: Shengping Zhang E-mail:s.zhang@hit.edu.cn
  • 作者简介:Yang Liu obtained her Ph.D. degree in computer science from Harbin Institute of Technology, Weihai, in 2015. She is currently a lecturer in the School of Computer Science and Technology at Harbin Institute of Technology, Weihai. Her current research interests include digital image processing and computer vision.
  • 基金资助:
    This work was supported in part by the National Natural Science Foundation of China under Nos. 61872112 and 61902092. Shengping Zhang was also supported by the Taishan Scholars Program of Shandong Province under Grant No. tsqn201812106.

Is It Easy to Recognize Baby's Age and Gender?

Yang Liu, Ruili He, Xiaoqian Lv, Wei Wang, Xin Sun, and Shengping Zhang*        

  1. School of Computer Science and Technology, Harbin Institute of Technology, Weihai 264209, China
  • Received:2021-01-27 Revised:2021-04-25 Online:2021-05-05 Published:2021-05-31
  • Contact: Shengping Zhang E-mail:s.zhang@hit.edu.cn
  • About author:Yang Liu obtained her Ph.D. degree in computer science from Harbin Institute of Technology, Weihai, in 2015. She is currently a lecturer in the School of Computer Science and Technology at Harbin Institute of Technology, Weihai. Her current research interests include digital image processing and computer vision.
  • Supported by:
    This work was supported in part by the National Natural Science Foundation of China under Nos. 61872112 and 61902092. Shengping Zhang was also supported by the Taishan Scholars Program of Shandong Province under Grant No. tsqn201812106.

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1.研究背景：近年来，从面部图像估计性别或年龄的面部分析任务已引起越来越多的关注。但是，大多数现有工作主要关注成人的脸部分析而忽略了婴儿的年龄估计和性别预测。缺乏对婴儿面部分析的研究有两个主要原因。第一个原因是人们没有意识到潜在的应用价值。实际上，分析婴儿的面部属性有很多应用，例如广告营销、智能育儿和科学育儿。0至2岁是婴儿成长的黄金时期，也为婴儿终生的身心健康奠定了坚实的基础。此外，由于国家政策的支持以及人们对婴儿的成长和发育的日益关注，育儿市场不断扩大，电子媒体内的育儿计划也在不断扩展。正确识别婴儿的年龄和性别对于改善科学育儿具有重要意义。第二个原因可能是现有数据集仅包含少量或没有婴儿图像。因为获取具有准确性别和年龄标签的婴儿数据集比较困难。
2.目的：由于智能育儿的市场需求不同扩大，同时学术界缺乏相关研究，所以本研究致力于探寻针对婴儿照片的婴儿的年龄估计和性别预测问题。
3.方法：我们首先为我们的研究收集了一个新的面部图像数据集，名为BabyFace，其中包含来自5872个2岁以下婴儿的15528张图像。除性别外，每个面部图像都标有0到24个月之间的年龄标签。此外，我们提出了新的年龄估计和性别识别方法。我们特别引入了注意力机制模块来解决BabyFace数据集上的年龄估计问题。受年龄估算方法的启发，在性别预测网络中我们使用了双流结构。
4.结果：我们使用BabyFace数据集对现有的相关方法进行实验对比，我们的年龄估算模型具有优秀的性能表现，估算误差不到2个月。所提出的性别预测算法在所有比较方法中都获得了最佳的准确性。相比于年龄估计，性别预测对于婴儿来说更加困难，因为婴儿的面部看不到胡须、毛孔等性别特征。
5.结论：该工作是第一个从婴儿的面部图像研究年龄和性别估计的方法，它是对现有成人性别和年龄估计方法的补充，可以为探索婴儿面部分析提供一些启发。

关键词: 年龄估计, 级别识别, 婴儿面部图像, 卷积神经网络, 注意力机制

Abstract: Face analysis tasks, e.g., estimating gender or age from a face image, have been attracting increasing interest in recent years. However, most existing studies focus mainly on analyzing an adult's face and ignore an interesting question:is it easy to estimate gender and age from a baby's face? In this paper, we explore this interesting problem. We first collect a new face image dataset for our research, named BabyFace, which contains 15 528 images from 5 872 babies younger than two years old. Besides gender, each face image is annotated with age in months from 0 to 24. In addition, we propose new age estimation and gender recognition methods. In particular, based on SSR-Net backbone, we introduce the attention mechanism module to solve the age estimation problem on the BabyFace dataset, named SSR-SE. In the part of gender recognition, inspired by the age estimation method, we also use a two-stream structure, named Two-Steam SE-block with Augment (TSSEAug). We extensively evaluate the performance of the proposed methods against the state-of-the-art methods on BabyFace. Our age estimation model achieves very appealing performance with an estimation error of less than two months. The proposed gender recognition method obtains the best accuracy among all compared methods. To the best of our knowledge, we are the first to study age estimation and gender recognition from a baby's face image, which is complementary to existing adult gender and age estimation methods and can shed some light on exploring baby face analysis.

Key words: age estimation, gender recognition, baby face, convolutional neural network (CNN), attention mechanism

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