Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal versus nominal and holistic versus local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.

中文翻译：

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异构人脸属性估计：一种深度多任务学习方法


人脸属性估计在视频监控、人脸检索和社交媒体方面有许多潜在的应用。虽然已经提出了许多用于面部属性估计的方法，但大多数方法在特征表示学习期间没有明确考虑属性相关性和异质性（例如，序数与名义、整体与局部）。在本文中，我们提出了一种深度多任务学习（DMTL）方法来联合估计单个人脸图像的多个异构属性。在 DMTL 中，我们使用卷积神经网络 (CNN) 来处理属性相关性和异质性，该网络包括所有属性的共享特征学习和异质属性的特定类别特征学习。我们还引入了无约束人脸数据库（LFW+），它是公共领域 LFW 的扩展，具有通过众包获得的异构人口统计属性（年龄、性别和种族）。具有多个人脸属性的基准（MORPH II、LFW+、CelebA、LFWA 和 FotW）的实验结果表明，与现有技术相比，所提出的方法具有优越的性能。最后，对具有单一属性的公共领域人脸数据库（LAP）的评估表明，该方法具有出色的泛化能力。