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图像视频序列中的人脸检测与识别
其他题名Face Detection and Recognition from Video Sequences
李江伟
学位类型工学博士
导师谭铁牛
2006-05-13
学位授予单位中国科学院研究生院
学位授予地点中国科学院自动化研究所
学位专业模式识别与智能系统
关键词图像视频序列 活体人脸判别 人脸检测 人脸识别 Video Sequence Liveness Detection Face Detection Face Recognition
摘要与静态图像相比,图像视频序列由动态场景下的多帧时间序列图像组成,序列中包含了丰富的时间和空间信息,这些因素使得人们普遍认为基于图像视频序列的人脸检测与识别方法比基于静态图像的方法具有更为广泛的应用前景。然而,视频序列中存在着各种不利于人脸检测与识别的因素,仍有很多理论与技术问题需待解决。在本论文中,通过分析如何充分利用视频序列中包含的时间和空间信息,对序列中的活体人脸判别、人脸检测和人脸识别等问题进行了初步的探讨。论文的主要工作及贡献如下: (1) 研究了在序列中判别活体人脸的可能途径,并提出了基于眼睛眨动分析的活体判别方法。通过分析序列眼睛图像边缘能量在多方向上的变化趋势,建立眼睛眨动判别模型。模型采用多判据融合机制,保证了活体判别的可靠性; (2) 按照静态人脸识别的思路,提出了一种基于关键帧选取的序列人脸识别框架,并详细推导了2DBayes算法。实验表明,尽管算法没有利用视频中包含的时间信息,但通过融合方法融合2DBayes对关键帧的识别结果,算法仍能取得较好的识别结果; (3) 详细讨论了目前video-to-video的序列识别算法的主要思想,并提出将现有的video-to-video算法分成贯序识别方法(Sequential Approach)和批量识别方法(Batch Approach)两大类。在充分考虑序列数据空间流形分布的基础上,将序列建模为聚类中心与聚类权重的集合,并提出了基于Earth Mover’s Distance的序列模型匹配方法; (4) 提出了一种基于GMM增量学习和模型更新的在线训练方法。我们认为在基于视频的应用中,算法应该具有自学习的功能。在设计初始人脸检测模型和人脸识别模型的基础上,通过增量学习,实现初始模型参数的自动更新,以得到新的人脸检测模型和人脸类别模型。在识别过程中,采用贝叶斯推理过程累积序列识别信息。实验显示了这种方法的有效性。
其他摘要Compared with still images, video image sequences are composed of multiple dynamic images and are abundant in temporal and spatial information. These properties have made most researchers believe that face detection and recognition from video is more promising than those from still images. However, there exist a number of difficulties in video, e.g., poor quality of video images, partial occlusion, large variations of image resolution, illumination and head pose, and so on. A number of open theoretical and practical problems remain to be solved. In this thesis, we put emphasis on how to make full use of both temporal and spatial information in video, and exploit such information to facilitate liveness detection, face detection and face recognition. The contributions of this thesis include: (1) Investigate the current approaches for liveness detection, and propose a novel method based on the analysis of eye blink. The method is established based on the observation that for the live faces, the edges along some scales and orientations vary consistently with eye blink. Compared with other approaches, our method is more reliable because of the fusion of multiple detectors for eye blink detection; (2) Propose a framework for face recognition in video by fusing the recognition results of the selected key frames. Especially, an algorithm named 2DBayes is introduced. Experimental results have showed that as long as good frames and recognition algorithms are selected, the framework can obtain good recognition performance though it does not take temporal information in video into account; (3) Present a detailed discussion on video-to-video algorithms. These algorithms can be typically divided into sequential approach and batch approach. The main problems of the batch methods are heavy computational load and coarse established models. To tackle these problems, using some dimensionality reduction techniques, a batch model named video signature is proposed to represent video data in feature space, and the similarity between two video signatures is measured by Earth Mover’s Distance; (4) Propose a novel approach to using GMM updating to solve the problems of face tracking and recognition in video. At first, by considering the differences between face tracking and recognition, two initial GMMs are designed for tracking and recognition purposes, respectively. Then, both models are updated with some online incremental learning algorithm so as to improve the tracking capability and obtain class-specific GMM. Finally, Bayesian inference is introduced into the recognition framework to accumulate the temporal information in video. Experimental results have demonstrated the effectiveness of this approach.
馆藏号XWLW980
其他标识符200218014603211
语种中文
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/5895
专题毕业生_博士学位论文
推荐引用方式
GB/T 7714
李江伟. 图像视频序列中的人脸检测与识别[D]. 中国科学院自动化研究所. 中国科学院研究生院,2006.
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