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Abstract：
Accurate face segmentation strongly benefits the human face image analysis problem. In this paper we propose a unified framework for face image analysis through end-to-end semantic face segmentation. The proposed framework contains a set of stack components for face understanding, which includes head pose estimation, age classification, and gender recognition. A manually labeled face data-set is used for training the Conditional Random Fields (CRFs) based segmentation model. A multi-class face segmentation framework developed through CRFs segments a facial image into six parts. The probabilistic classification strategy is used, and probability maps are generated for each class. The probability maps are used as features descriptors and a Random Decision Forest (RDF) classifier is modeled for each task (head pose, age, and gender). We assess the performance of the proposed framework on several data-sets and report better results as compared to the previously reported results.
摘要：
准确的人脸分割大大有利于人脸图像分析问题。在本文中，我们提出了通过端到端语义人脸分割进行人脸图像分析的统一框架。所提出的框架包含一组用于面部理解的堆栈组件，其中包括头部姿势估计，年龄分类,和性别认同。手动标记的面部数据集用于训练基于条件随机场(CRF)的分割模型。通过CRF开发的多类别面部分割框架将面部图像分割为六个部分。采用概率分类策略，并为每个类生成概率图。概率图用作特征描述符，并为每个任务（头部姿势，年龄,和性别）。我们在几个数据集上评估拟议框架的性能，并报告与先前报告的结果相比更好的结果。
