项目名称: 非约束环境下人脸多属性分析的理论与方法研究
项目编号: No.61472386
项目类型: 面上项目
立项/批准年度: 2015
项目学科: 其他
项目作者: 周曦
作者单位: 中国科学院重庆绿色智能技术研究院
项目金额: 81万元
中文摘要: 人脸属性识别是指基于面部特征来对人脸的性别、表情、种族、年龄等属性做出判断,在人脸图像检索、可疑人员监控等多个领域具有广阔的应用前景。现有人脸属性识别方法通常只提取低层图像特征、对属性之间相关性的利用不够充分;另外由于光照、角度、遮挡、图像质量等因素的影响,现有方法对环境变化不够鲁棒。本项目将围绕非约束环境下人脸属性的表示与识别这一关键科学问题,针对非约束环境下的人脸图像正规化、人脸属性特征的学习、人脸属性之间相关性的利用等理论与方法开展研究,重点突破基于自适应多列栈式稀疏去噪自编码器的人脸图像正规化、基于深度卷积神经网络的多种人脸属性特征学习、融合属性之间相关性的分类器设计及多属性分类结果融合等关键技术。项目将开发基于多属性识别的人脸图像实时检索系统,其性能达到国际同类算法的领先水平,为大规模视频监控网络下可疑人员监控等重大安防应用奠定基础。
中文关键词: 人脸属性识别;深度学习;卷积神经网络
英文摘要: Facial attribute recognition is to estimate the facial attributes, such as gender, expression, ethnicity and age etc., based on the extracted facial features. It is widely used in the fields of face image search, suspicious person monitoring, etc. Existing facial attribute recognition methods usually extract low-level visual features and do not use the correlations between the attributes sufficiently. Moreover, these methods are not robust to environment change due to the effects of illumination, pose, occlusion, image quality, etc. Focusing on the scientific problem of facial attribute representation and recognition in unconstrained environment, this project aims to investigate the theory and methods of face image normalization in unconstrained environment, facial attribute feature learning and the usage of correlations between facial attributes, and to break through the key technologies of face image normalization based on adaptive multi-column stacked sparse denosing autoencoder, multi-attribute feature learning based on deep convolutional neural networks, and classifier design by incorporating the correlations between the attributes and fusing multi-attribute classification results. This project will develop a real-time face search system based on multi-attribute recognition, which can reach the state-of-the-art performance and lay the foundation for the significant security applications like suspicious person monitoring under large-scale video surveillance.
英文关键词: facial attribute recognition;deep learning;convolutional neural network