Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)最新文献_第9页

3D human face recognition using point signature 基于点签名的三维人脸识别

Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) Pub Date : 2000-03-26 DOI: 10.1109/AFGR.2000.840640

C. Chua, F. Han, Yeong-Khing Ho

引用次数: 338

Analysis and synthesis of pose variations of human faces by a linear PCMAP model and its application for pose-invariant face recognition system 基于线性PCMAP模型的人脸姿态变化分析与综合及其在姿态不变人脸识别系统中的应用

Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) Pub Date : 2000-03-26 DOI: 10.1109/AFGR.2000.840625

K. Okada, S. Akamatsu, C. Malsburg

引用次数: 31

Automatic face modeling from monocular image sequences using modified non parametric regression and an affine camera model 利用改进的非参数回归和仿射相机模型对单眼图像序列进行人脸自动建模

Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) Pub Date : 2000-03-26 DOI: 10.1109/AFGR.2000.840684

K. Sengupta, Wang Shiqin, C. Ko, P. Burman

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引用次数: 3

The global dimensionality of face space 面空间的全局维数

Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580) Pub Date : 2000-03-26 DOI: 10.1109/AFGR.2000.840645

Penio S. Penev, L. Sirovich

{"title":"The global dimensionality of face space","authors":"Penio S. Penev, L. Sirovich","doi":"10.1109/AFGR.2000.840645","DOIUrl":"https://doi.org/10.1109/AFGR.2000.840645","url":null,"abstract":"A low-dimensional representation of sensory signals is the key to solving many of the computational problems encountered in high-level vision. Principal component analysis (PCA) has been used in the past to derive such compact representations for the object class of human faces. Here, with an interpretation of PCA as a probabilistic model, we employ two objective criteria to study its generalization properties in the context of large frontal-pose face databases. We find that the eigenfaces, the eigenspectrum, and the generalization depend strongly on the ensemble composition and size, with statistics for populations as large as 5500, still not stationary. Further, the assumption of mirror symmetry of the ensemble improves the quality of the results substantially in the low-statistics regime, and is also essential in the high-statistics regime. We employ a perceptual criterion and argue that, even with large statistics, the dimensionality of the PCA subspace necessary for adequate representation of the identity information in relatively tightly cropped faces is in the 400-700 range, and we show that a dimensionality of 200 is inadequate. Finally, we discuss some of the shortcomings of PCA and suggest possible solutions.","PeriodicalId":360065,"journal":{"name":"Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121760568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 102