Facial composites are typically constructed by witnesses to crime by describing a suspect's face and then selecting facial features from a kit of parts. Unfortunately, when produced in this way, composites are very poorly identified. In contrast, there is mounting evidence that other, more recognition-based approaches can produce a much better likeness of a suspect. With the EvoFIT system, for example, witnesses are presented with sets of complete faces and a composite is `evolved' through a process of selection and breeding. The current work serves to augment EvoFIT by developing a set of psychologically useful `knobs' that allow faces to be manipulated along dimensions such as facial weight, masculinity, and age. These holistic dimensions were implemented by increasing the size and variability of the underlying face model and obtaining perceptual ratings so that the space could be suitably vectorised. Two evaluations suggested that the new dimensions were operating appropriately
{"title":"Adding holistic dimensions to a facial composite system","authors":"C. Frowd, V. Bruce, A. McIntyre, P. Hancock","doi":"10.1109/FGR.2006.20","DOIUrl":"https://doi.org/10.1109/FGR.2006.20","url":null,"abstract":"Facial composites are typically constructed by witnesses to crime by describing a suspect's face and then selecting facial features from a kit of parts. Unfortunately, when produced in this way, composites are very poorly identified. In contrast, there is mounting evidence that other, more recognition-based approaches can produce a much better likeness of a suspect. With the EvoFIT system, for example, witnesses are presented with sets of complete faces and a composite is `evolved' through a process of selection and breeding. The current work serves to augment EvoFIT by developing a set of psychologically useful `knobs' that allow faces to be manipulated along dimensions such as facial weight, masculinity, and age. These holistic dimensions were implemented by increasing the size and variability of the underlying face model and obtaining perceptual ratings so that the space could be suitably vectorised. Two evaluations suggested that the new dimensions were operating appropriately","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132568729","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}
Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we propose a novel, general recognition framework for efficient matching of individual face images, sets or sequences. The framework is based on simple image processing filters that compete with unprocessed greyscale input to yield a single matching score between individuals. It is shown how the discrepancy between illumination conditions between novel input and the training data set can be estimated and used to weigh the contribution of two competing representations. We describe an extensive empirical evaluation of the proposed method on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our algorithm consistently demonstrated a dramatic performance improvement over traditional filtering approaches. We demonstrate a reduction of 50-75% in recognition error rates, the best performing method-filter combination correctly recognizing 96% of the individuals
{"title":"A new look at filtering techniques for illumination invariance in automatic face recognition","authors":"Ognjen Arandjelovic, R. Cipolla","doi":"10.1109/FGR.2006.14","DOIUrl":"https://doi.org/10.1109/FGR.2006.14","url":null,"abstract":"Illumination invariance remains the most researched, yet the most challenging aspect of automatic face recognition. In this paper we propose a novel, general recognition framework for efficient matching of individual face images, sets or sequences. The framework is based on simple image processing filters that compete with unprocessed greyscale input to yield a single matching score between individuals. It is shown how the discrepancy between illumination conditions between novel input and the training data set can be estimated and used to weigh the contribution of two competing representations. We describe an extensive empirical evaluation of the proposed method on 171 individuals and over 1300 video sequences with extreme illumination, pose and head motion variation. On this challenging data set our algorithm consistently demonstrated a dramatic performance improvement over traditional filtering approaches. We demonstrate a reduction of 50-75% in recognition error rates, the best performing method-filter combination correctly recognizing 96% of the individuals","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130228942","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}
Huazhong Ning, T. Han, Yuxiao Hu, ZhenQiu Zhang, Yun Fu, Thomas S. Huang
A realtime system for shrug detection is discussed in this paper. The system is automatically initialized by a face detector based on Ada-boost [P. Viola and M. Jones, May 2004]. After frontal face is localized by the face detector, shoulder position is detected by fitting a parabola to the nearby horizontal edges using weighted Hough transform [K. Sugawara, 1997]. Since shrug is an action which is defined not only by the distance between face and shoulder but also the relative temporal-spatial changing between them, we propose a parameterizing scheme using two different parabolas, named as "stable parabola" (SP) and "transient parabola" (TP) to characterize the action shrug. Stable parabola represents the mean shoulder position over a long time duration, while transient parabola represents the mean shoulder position of a very short time duration. By using this scheme (only 6 dimensions), we avoid the high dimensional representation of the temporal process-shrug, and therefore make the realtime implementation possible. The shrug detector is then trained in the parameter space using Fisher discriminant analysis (FDA). The experiments show that the proposed shrug detector is able to not only detect the shrug action correctly and efficiently (in realtime), but also tolerate the large in-class variation caused by different subject, different action speed, illumination, partial occlusion, and background clutter. So the proposed realtime shrug detector is promising in video analysis under an uncontrolled environment
本文讨论了一种实时耸肩检测系统。系统由基于Ada-boost的人脸检测器自动初始化[P]。Viola and M. Jones, 2004年5月]。人脸检测器对正面人脸进行定位后，利用加权霍夫变换[K]对附近水平边缘拟合抛物线来检测肩部位置。Sugawara, 1997]。由于耸耸肩是一个动作，它不仅是由脸和肩膀之间的距离定义的，而且是它们之间的相对时空变化，我们提出了一个参数化方案，使用两种不同的抛物线，称为“稳定抛物线”(SP)和“瞬态抛物线”(TP)来表征动作耸耸肩。稳定抛物线表示长时间内肩膀的平均位置，而瞬态抛物线表示很短时间内肩膀的平均位置。通过使用这种方案(只有6个维度)，我们避免了时态过程耸耸肩的高维表示，因此使实时实现成为可能。然后使用Fisher判别分析(FDA)在参数空间中训练耸肩检测器。实验表明，所提出的耸肩检测器不仅能够正确有效地检测耸肩动作(实时)，而且能够容忍不同主体、不同动作速度、光照、局部遮挡和背景杂波等引起的类内较大变化。因此，所提出的实时耸肩检测器在非受控环境下的视频分析中具有广阔的应用前景
{"title":"A realtime shrug detector","authors":"Huazhong Ning, T. Han, Yuxiao Hu, ZhenQiu Zhang, Yun Fu, Thomas S. Huang","doi":"10.1109/FGR.2006.15","DOIUrl":"https://doi.org/10.1109/FGR.2006.15","url":null,"abstract":"A realtime system for shrug detection is discussed in this paper. The system is automatically initialized by a face detector based on Ada-boost [P. Viola and M. Jones, May 2004]. After frontal face is localized by the face detector, shoulder position is detected by fitting a parabola to the nearby horizontal edges using weighted Hough transform [K. Sugawara, 1997]. Since shrug is an action which is defined not only by the distance between face and shoulder but also the relative temporal-spatial changing between them, we propose a parameterizing scheme using two different parabolas, named as \"stable parabola\" (SP) and \"transient parabola\" (TP) to characterize the action shrug. Stable parabola represents the mean shoulder position over a long time duration, while transient parabola represents the mean shoulder position of a very short time duration. By using this scheme (only 6 dimensions), we avoid the high dimensional representation of the temporal process-shrug, and therefore make the realtime implementation possible. The shrug detector is then trained in the parameter space using Fisher discriminant analysis (FDA). The experiments show that the proposed shrug detector is able to not only detect the shrug action correctly and efficiently (in realtime), but also tolerate the large in-class variation caused by different subject, different action speed, illumination, partial occlusion, and background clutter. So the proposed realtime shrug detector is promising in video analysis under an uncontrolled environment","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"27 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132116544","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}
In this paper, a novel sparse feature set is introduced into the Adaboost learning framework for multi-view face detection (MVFD), and a learning algorithm based on heuristic search is developed to select sparse features in granular space. Compared with Haar-like features, sparse features are more generic and powerful to characterize multi-view face pattern that is more diverse and asymmetric than frontal face pattern. In order to cut down search space to a manageable size, we propose a multi-scaled search algorithm that is about 6 times faster than brute-force search. With this method, a MVFD system is implemented that covers face pose changes over +/-45deg rotation in plane (RIP) and +/-90deg rotation off plane (ROP). Experiments over well-know test set are reported to show its high performance in both accuracy and speed
{"title":"Learning sparse features in granular space for multi-view face detection","authors":"Chang Huang, H. Ai, Yuan Li, S. Lao","doi":"10.1109/FGR.2006.70","DOIUrl":"https://doi.org/10.1109/FGR.2006.70","url":null,"abstract":"In this paper, a novel sparse feature set is introduced into the Adaboost learning framework for multi-view face detection (MVFD), and a learning algorithm based on heuristic search is developed to select sparse features in granular space. Compared with Haar-like features, sparse features are more generic and powerful to characterize multi-view face pattern that is more diverse and asymmetric than frontal face pattern. In order to cut down search space to a manageable size, we propose a multi-scaled search algorithm that is about 6 times faster than brute-force search. With this method, a MVFD system is implemented that covers face pose changes over +/-45deg rotation in plane (RIP) and +/-90deg rotation off plane (ROP). Experiments over well-know test set are reported to show its high performance in both accuracy and speed","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133867121","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}
This paper presents an approach to recognising the gender and expression of face images by means of active appearance models (AAM). Features extracted by a trained AAM are used to construct support vector machine (SVM) classifiers for 4 elementary emotional states (happy, angry, sad, neutral). These classifiers are arranged into a cascade structure in order to optimise overall recognition performance. Furthermore, it is shown how performance can be further improved by first classifying the gender of the face images using an SVM trained in a similar manner. Both gender-specific expression classification and expression-specific gender classification cascades are considered, with the former yielding better recognition performance. We conclude that there are gender-specific differences in the appearance of facial expressions that can be exploited for automated recognition, and that cascades are an efficient and effective way of performing multi-class recognition of facial expressions
{"title":"Cascaded classification of gender and facial expression using active appearance models","authors":"Yunus Saatci, C. Town","doi":"10.1109/FGR.2006.29","DOIUrl":"https://doi.org/10.1109/FGR.2006.29","url":null,"abstract":"This paper presents an approach to recognising the gender and expression of face images by means of active appearance models (AAM). Features extracted by a trained AAM are used to construct support vector machine (SVM) classifiers for 4 elementary emotional states (happy, angry, sad, neutral). These classifiers are arranged into a cascade structure in order to optimise overall recognition performance. Furthermore, it is shown how performance can be further improved by first classifying the gender of the face images using an SVM trained in a similar manner. Both gender-specific expression classification and expression-specific gender classification cascades are considered, with the former yielding better recognition performance. We conclude that there are gender-specific differences in the appearance of facial expressions that can be exploited for automated recognition, and that cascades are an efficient and effective way of performing multi-class recognition of facial expressions","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129516394","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}
This paper describes the video-based face recognition evaluation performed under the CHIL project and the systems that participated to it, along with the obtained first year results. The evaluation methodology comprises a specially built database of videos and an evaluation protocol. Two complete automatic face detection and recognition systems from two academic institutions participated to the evaluation. For comparison purposes, a baseline system is also developed using well-known methods for face detection and recognition
{"title":"Video-based face recognition evaluation in the CHIL project - Run 1","authors":"H. K. Ekenel, Aristodemos Pnevmatikakis","doi":"10.1109/FGR.2006.110","DOIUrl":"https://doi.org/10.1109/FGR.2006.110","url":null,"abstract":"This paper describes the video-based face recognition evaluation performed under the CHIL project and the systems that participated to it, along with the obtained first year results. The evaluation methodology comprises a specially built database of videos and an evaluation protocol. Two complete automatic face detection and recognition systems from two academic institutions participated to the evaluation. For comparison purposes, a baseline system is also developed using well-known methods for face detection and recognition","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127098320","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}
This paper explores a form of robust distance measures for biometrics and presents experiments showing that, when applied per "class" they can dramatically improve the accuracy of face recognition. We "robustify'' many distance measures included in the CSU face-recognition toolkit, and apply them to PCA, LDA and EBGM. The resulting performance puts each of these algorithms, for the FERET datasets tested, on par with commercial face recognition results. Unlike passwords, biometric signatures cannot be changed or revoked. This paper shows how the robust distance measures introduce can be used for secure robust revocable biometrics. The technique produces what we call Biotopestrade, which provide public-key cryptographic security, supports matching in encoded form, cannot be linked across different databases and are revocable. Biotopes support a robust distance measure computed on the encoded form that is proven not to decrease, and that may potentially increase, accurately. The approach is demonstrated, to improve performance beyond the already impressive gains from the robust distance measure
{"title":"Robust distance measures for face-recognition supporting revocable biometric tokens","authors":"T. Boult","doi":"10.1109/FGR.2006.94","DOIUrl":"https://doi.org/10.1109/FGR.2006.94","url":null,"abstract":"This paper explores a form of robust distance measures for biometrics and presents experiments showing that, when applied per \"class\" they can dramatically improve the accuracy of face recognition. We \"robustify'' many distance measures included in the CSU face-recognition toolkit, and apply them to PCA, LDA and EBGM. The resulting performance puts each of these algorithms, for the FERET datasets tested, on par with commercial face recognition results. Unlike passwords, biometric signatures cannot be changed or revoked. This paper shows how the robust distance measures introduce can be used for secure robust revocable biometrics. The technique produces what we call Biotopestrade, which provide public-key cryptographic security, supports matching in encoded form, cannot be linked across different databases and are revocable. Biotopes support a robust distance measure computed on the encoded form that is proven not to decrease, and that may potentially increase, accurately. The approach is demonstrated, to improve performance beyond the already impressive gains from the robust distance measure","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132861995","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}
P. Dreuw, Thomas Deselaers, David Rybach, Daniel Keysers, H. Ney
We present a novel tracking algorithm that uses dynamic programming to determine the path of target objects and that is able to track an arbitrary number of different objects. The traceback method used to track the targets avoids taking possibly wrong local decisions and thus reconstructs the best tracking paths using the whole observation sequence. The tracking method can be compared to the nonlinear time alignment in automatic speech recognition (ASR) and it can analogously be integrated into a hidden Markov model based recognition process. We show how the method can be applied to the tracking of hands and the face for automatic sign language recognition
{"title":"Tracking using dynamic programming for appearance-based sign language recognition","authors":"P. Dreuw, Thomas Deselaers, David Rybach, Daniel Keysers, H. Ney","doi":"10.1109/FGR.2006.107","DOIUrl":"https://doi.org/10.1109/FGR.2006.107","url":null,"abstract":"We present a novel tracking algorithm that uses dynamic programming to determine the path of target objects and that is able to track an arbitrary number of different objects. The traceback method used to track the targets avoids taking possibly wrong local decisions and thus reconstructs the best tracking paths using the whole observation sequence. The tracking method can be compared to the nonlinear time alignment in automatic speech recognition (ASR) and it can analogously be integrated into a hidden Markov model based recognition process. We show how the method can be applied to the tracking of hands and the face for automatic sign language recognition","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114163299","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}
When delivering visual content on multiple devices and services, faces can often become unrecognisable. This paper draws together research from across the cognitive psychology literature to argue that faces should be treated as a special case when rendering content. Where available, we suggest methods by which recognition can be imp roved within the constraints of the device and service. Firstly, we review the psychology literature to discuss recognition performance when manipulating the face's scale, colour palette, orientation and motion. Secondly, we consider how characteristics of the individual faces can aide or hinder recognition and how caricature may be applied, especially within crowds, to improve it. Thirdly, we show how context can make even the most abstract faces recognisable. Fourthly, we highlight the challenges of making a good portrait, beyond the criteria of simply being recognisable. Finally, we begin to describe a framework for automatically rendering faces 'smartly', such that they will be most recognisable given the device and service of which they are a part
{"title":"Making recognisable faces","authors":"D. Chatting","doi":"10.1109/FGR.2006.76","DOIUrl":"https://doi.org/10.1109/FGR.2006.76","url":null,"abstract":"When delivering visual content on multiple devices and services, faces can often become unrecognisable. This paper draws together research from across the cognitive psychology literature to argue that faces should be treated as a special case when rendering content. Where available, we suggest methods by which recognition can be imp roved within the constraints of the device and service. Firstly, we review the psychology literature to discuss recognition performance when manipulating the face's scale, colour palette, orientation and motion. Secondly, we consider how characteristics of the individual faces can aide or hinder recognition and how caricature may be applied, especially within crowds, to improve it. Thirdly, we show how context can make even the most abstract faces recognisable. Fourthly, we highlight the challenges of making a good portrait, beyond the criteria of simply being recognisable. Finally, we begin to describe a framework for automatically rendering faces 'smartly', such that they will be most recognisable given the device and service of which they are a part","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123533793","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}
The computer is an ubiquitous element of modern society, nonetheless, human computer interaction is still rather inflexible. Particularly in local collaborative environments, like office meetings, the property that the mouse and keyboard exhibit of being a gateway for the individual to act upon a workspace, makes local computer mediated collaboration uncomfortable, as users have to time-share their actions upon that workspace. We present in this paper a novel, very fast, vision based interface that allows multiple users to interact simultaneously with a single computer by performing hand gestures, which are filmed by a static video camera. This interface attempts to continuously recognize predefined postures and movements using a view-dependent method. We also present A.C.O, an application which receives input from the vision based interface and allows users around a table to collaborate playing synthesized music instruments by moving their hands
{"title":"A Vision Based Interface for Local Collaborative Music Synthesis","authors":"João Carreira, P. Peixoto","doi":"10.1109/FGR.2006.16","DOIUrl":"https://doi.org/10.1109/FGR.2006.16","url":null,"abstract":"The computer is an ubiquitous element of modern society, nonetheless, human computer interaction is still rather inflexible. Particularly in local collaborative environments, like office meetings, the property that the mouse and keyboard exhibit of being a gateway for the individual to act upon a workspace, makes local computer mediated collaboration uncomfortable, as users have to time-share their actions upon that workspace. We present in this paper a novel, very fast, vision based interface that allows multiple users to interact simultaneously with a single computer by performing hand gestures, which are filmed by a static video camera. This interface attempts to continuously recognize predefined postures and movements using a view-dependent method. We also present A.C.O, an application which receives input from the vision based interface and allows users around a table to collaborate playing synthesized music instruments by moving their hands","PeriodicalId":109260,"journal":{"name":"7th International Conference on Automatic Face and Gesture Recognition (FGR06)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125678126","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}
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