Predicting Personal Traits from Facial Images Using Convolutional Neural Networks Augmented with Facial Landmark Information
Predicting Personal Traits from Facial Images Using Convolutional Neural Networks Augmented with Facial Landmark Information
复制标题
使用增强了面部标志信息的卷积神经网络从面部图像预测个人特征
DOI：
10.17863/cam.7611
复制
发表时间：
2016
期刊：
影响因子：
5.5
通讯作者：
A. Criminisi
中科院分区：
文献类型：
--
作者：
Yoad Lewenberg;Yoram Bachrach;S. Shankar;A. Criminisi
我们考虑的任务是根据一个人的面部图像来预测他的各种特征。我们的目标是估计性别、种族和年龄等特征，以及更主观的特征，如一个人表达的情感，或者他们是否幽默或有吸引力。由于最近对深度卷积神经网络（CNN）的研究激增，我们开始使用CNN架构，并证实CNN有希望用于面部属性预测。为了进一步提高性能，我们提出了一种新的方法，将输入图像的面部地标信息作为额外的通道，帮助CNN学习面部特定的特征，以便各种训练图像的地标保持对应。我们实证分析了我们提出的方法的性能，显示出跨特征的基线一致的改善。我们在一个相当大的人脸属性数据集（FAD）上展示了我们的系统，该数据集包含大约20万个标签，用于10个最受欢迎的特征，用于超过10，000张面部图像。
We consider the task of predicting various traits of a person given an image of their face. We aim to estimate traits such as gender, ethnicity and age, as well as more subjective traits as the emotion a person expresses or whether they are humorous or attractive. Due to the recent surge of research on Deep Convolutional Neural Networks (CNNs), we begin by using a CNN architecture, and corroborate that CNNs are promising for facial attribute prediction. To further improve performance, we propose a novel approach that incorporates facial landmark information for input images as an additional channel, helping the CNN learn face-specific features so that the landmarks across various training images hold correspondence. We empirically analyze the performance of our proposed method, showing consistent improvement over the baselines across traits. We demonstrate our system on a sizeable Face Attributes Dataset (FAD), comprising of roughly 200,000 labels, for 10 most sought-after traits, for over 10,000 facial images.