Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05)最新文献

A model-based sampling and sample synthesis method for auto identification in computer vision 一种基于模型的计算机视觉自动识别采样与样本合成方法

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.5

Nanfei Sun, N. Haas, J. Connell, Sharath Pankanti

{"title":"A model-based sampling and sample synthesis method for auto identification in computer vision","authors":"Nanfei Sun, N. Haas, J. Connell, Sharath Pankanti","doi":"10.1109/AUTOID.2005.5","DOIUrl":"https://doi.org/10.1109/AUTOID.2005.5","url":null,"abstract":"The need for a large sample size grows exponentially with the dimensionality of the feature space (\"curse of dimensionality\"), which increases the labor cost during the training procedure and severely restricts the number of the practical applications. While feature selection methods can often alleviate the problems associated with the curse of dimensionality, complex large scale pattern recognition problems may not be amenable to features selection approach due to large intrinsic dimensionality. In such situations, the only effective solution to conquer the complications of the high-dimensional functions is to incorporate knowledge about the data that is correct. How to incorporate the domain knowledge with the specific machine learning system has been widely studied in the pattern classification field. In this paper, we will explore a novel method to synthesize a larger, valid training sample data set based on a smaller set of the key samples that are collected by a model based sampling theory that incorporates the domain knowledge of the computer vision. In addition to reducing the training sample size in the learning procedure, our emphasis is on providing practical advice on how to incorporate domain knowledge to design and simplify a vision based pattern classification model.","PeriodicalId":206458,"journal":{"name":"Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116678581","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: 4

Face class code based feature extraction for face recognition 基于人脸类代码的人脸识别特征提取

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.22

C. Xie, B. Kumar

{"title":"Face class code based feature extraction for face recognition","authors":"C. Xie, B. Kumar","doi":"10.1109/AUTOID.2005.22","DOIUrl":"https://doi.org/10.1109/AUTOID.2005.22","url":null,"abstract":"In face recognition, the goal is to assign a class label for a test image of a subject from N classes in the database, when binary classifiers are used, the commonly used method is the one-per-class (OPC) i.e., one classifier per subject. A drawback of the OPC method is that when the number of classes is large, it takes very long time to make a classification decision. In place of the computationally-demanding OPC method, we propose a new feature extraction method \"face class code\" (FCC) based on binary classifiers. For example, correlation filters and support vector machines can be used to generate feature vectors to deal with large number of classes. The FCC method encodes each class label into a binary string, and we design classifiers to discriminate '1' or '0' for each bit in the sequence, to determine the class label. Thus, we will need as few as [log/sub 2/(N)] binary classifiers to achieve an N-class recognition problem. This binary coding framework also opens the whole world of error control codes (ECC), which can be used to improve the recognition performance. The proposed method is verified through experiments on the PIE database and the AR database.","PeriodicalId":206458,"journal":{"name":"Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130903368","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: 7

Face recognition in the presence of expression and/or illumination variation 面部表情和/或光照变化时的面部识别

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.23

H. Sellahewa, S. Jassim

引用次数: 11

Fusion of methods for keystroke dynamic authentication 按键动态认证方法的融合

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.30

Sylvain Hocquet, Jean-Yves Ramel, H. Cardot

引用次数: 39

PCA vs. automatically pruned wavelet-packet PCA for illumination tolerant face recognition PCA与自动修剪小波包PCA在光照耐受人脸识别中的比较

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.38

Ramamurthy Bhagavatula, M. Savvides

{"title":"PCA vs. automatically pruned wavelet-packet PCA for illumination tolerant face recognition","authors":"Ramamurthy Bhagavatula, M. Savvides","doi":"10.1109/AUTOID.2005.38","DOIUrl":"https://doi.org/10.1109/AUTOID.2005.38","url":null,"abstract":"Facial recognition/verification R. Chellappa et al., (1995), is a continuing and growing area of research in the field of biometrics. One of the first approaches to this challenge was principal component analysis (PCA) [M. A. Turk et al., (1991), T. Chen et al., (2002)]. Typically PCA is performed in the original spatial domain. However, PCA has a high sensitivity to illumination effects in the original spatial domain. We propose that by using wavelet packet decomposition M. Vetterli et al., (1995), to create localized space-frequency subspaces of the original data, we can perform PCA in these subspaces which can generalize better across illumination variations. We report results on the CMU PIE database T. Sim et al., (2003), by comparing reconstruction error in the original spatial domain to that of the reconstruction error in the spatial subspaces (keeping same number of eigenvectors). It is seen that the total reconstruction error of the space-frequency subspaces is smaller than that of the original space and the automatically pruned wavelet packet PCA produced better face recognition performance across illumination.","PeriodicalId":206458,"journal":{"name":"Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133414516","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: 5

Face recognition with renewable and privacy preserving binary templates 具有可更新和保护隐私的二进制模板的人脸识别

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.24

T. Kevenaar, G. Schrijen, M. V. D. Veen, A. Akkermans, F. Zuo

引用次数: 154

Multi-tag radio frequency identification systems 多标签射频识别系统

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.36

L. Bolotnyy, G. Robins

引用次数: 29

Hand geometry: a new approach for feature extraction 手几何:一种特征提取的新方法

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.33

Guilherme Boreki, A. Zimmer

引用次数: 39

A real-time image selection algorithm: fingerprint recognition using mobile devices with embedded camera 一种实时图像选择算法:利用内置摄像头的移动设备进行指纹识别

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.6

Dongjae Lee, Wonchurl Jang, Doeksoo Park, Sung-Jae Kim, Jaihie Kim

引用次数: 7

Design of UHF RFID emulators with applications to RFID testing and data transport 超高频RFID仿真器的设计及其在RFID测试和数据传输中的应用

Fourth IEEE Workshop on Automatic Identification Advanced Technologies (AutoID'05) Pub Date : 2005-10-17 DOI: 10.1109/AUTOID.2005.18

Richard Redemske, R. Fletcher

引用次数: 37