Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383284
S. Romdhani, T. Vetter
This paper presents a novel statistical shape model that can be used to detect and localise feature points of a class of objects in images. The shape model is inspired from the 3D morphable model (3DMM) and has the property to be viewpoint invariant. This shape model is used to estimate the probability of the position of a feature point given the position of reference feature points, accounting for the uncertainty of the position of the reference points and of the intrinsic variability of the class of objects. The viewpoint invariant detection algorithm maximises a foreground/background likelihood ratio of the relative position of the feature points, their appearance, scale, orientation and occlusion state. Computational efficiency is obtained by using the Bellman principle and an early rejection rule based on 3D to 2D projection constraints. Evaluations of the detection algorithm on the CMU-P1E face images and on a large set of non-face images show high levels of accuracy (zero false alarms for more than 90% detection rate). As well as locating feature points, the detection algorithm also estimates the pose of the object and a few shape parameters. It is shown that it can be used to initialise a 3DMM fitting algorithm and thus enables a fully automatic viewpoint and lighting invariant image analysis solution.
{"title":"3D Probabilistic Feature Point Model for Object Detection and Recognition","authors":"S. Romdhani, T. Vetter","doi":"10.1109/CVPR.2007.383284","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383284","url":null,"abstract":"This paper presents a novel statistical shape model that can be used to detect and localise feature points of a class of objects in images. The shape model is inspired from the 3D morphable model (3DMM) and has the property to be viewpoint invariant. This shape model is used to estimate the probability of the position of a feature point given the position of reference feature points, accounting for the uncertainty of the position of the reference points and of the intrinsic variability of the class of objects. The viewpoint invariant detection algorithm maximises a foreground/background likelihood ratio of the relative position of the feature points, their appearance, scale, orientation and occlusion state. Computational efficiency is obtained by using the Bellman principle and an early rejection rule based on 3D to 2D projection constraints. Evaluations of the detection algorithm on the CMU-P1E face images and on a large set of non-face images show high levels of accuracy (zero false alarms for more than 90% detection rate). As well as locating feature points, the detection algorithm also estimates the pose of the object and a few shape parameters. It is shown that it can be used to initialise a 3DMM fitting algorithm and thus enables a fully automatic viewpoint and lighting invariant image analysis solution.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130841268","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}
Image segmentation plays an important role in computer vision and image analysis. In this paper, the segmentation problem is formulated as a labeling problem under a probability maximization framework. To estimate the label configuration, an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori (MAP) estimation and the maximum-likelihood (ML) estimation. The MAP estimation problem is modeled with Markov random fields (MRFs). A graph-cut algorithm is used to find the solution to the MAP-MRF estimation. The ML estimation is achieved by finding the means of region features. Our algorithm can automatically segment an image into regions with relevant textures or colors without the need to know the number of regions in advance. In addition, under the same framework, it can be extended to another algorithm that extracts objects of a particular class from a group of images. Extensive experiments have shown the effectiveness of our approach.
{"title":"Iterative MAP and ML Estimations for Image Segmentation","authors":"Shifeng Chen, Liangliang Cao, Jianzhuang Liu, Xiaoou Tang","doi":"10.1109/CVPR.2007.383007","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383007","url":null,"abstract":"Image segmentation plays an important role in computer vision and image analysis. In this paper, the segmentation problem is formulated as a labeling problem under a probability maximization framework. To estimate the label configuration, an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori (MAP) estimation and the maximum-likelihood (ML) estimation. The MAP estimation problem is modeled with Markov random fields (MRFs). A graph-cut algorithm is used to find the solution to the MAP-MRF estimation. The ML estimation is achieved by finding the means of region features. Our algorithm can automatically segment an image into regions with relevant textures or colors without the need to know the number of regions in advance. In addition, under the same framework, it can be extended to another algorithm that extracts objects of a particular class from a group of images. Extensive experiments have shown the effectiveness of our approach.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"199 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130418175","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}
Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383444
A. Leykin, Yang Ran, R. Hammoud
The paper presents a fusion-tracker and pedestrian classifier for color and thermal cameras. The tracker builds a background model as a multi-modal distribution of colors and temperatures. It is constructed as a particle filter that makes a number of informed reversible transformations to sample the model probability space in order to maximize posterior probability of the scene model. Observation likelihoods of moving objects account their 3D locations with respect to the camera and occlusions by other tracked objects as well as static obstacles. After capturing the coordinates and dimensions of moving objects we apply a pedestrian classifier based on periodic gait analysis. To separate humans from other moving objects, such as cars, we detect, in human gait, a symmetrical double helical pattern, that can then be analyzed using the Frieze Group theory. The results of tracking on color and thermal sequences demonstrate that our algorithm is robust to illumination noise and performs well in the outdoor environments.
{"title":"Thermal-Visible Video Fusion for Moving Target Tracking and Pedestrian Classification","authors":"A. Leykin, Yang Ran, R. Hammoud","doi":"10.1109/CVPR.2007.383444","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383444","url":null,"abstract":"The paper presents a fusion-tracker and pedestrian classifier for color and thermal cameras. The tracker builds a background model as a multi-modal distribution of colors and temperatures. It is constructed as a particle filter that makes a number of informed reversible transformations to sample the model probability space in order to maximize posterior probability of the scene model. Observation likelihoods of moving objects account their 3D locations with respect to the camera and occlusions by other tracked objects as well as static obstacles. After capturing the coordinates and dimensions of moving objects we apply a pedestrian classifier based on periodic gait analysis. To separate humans from other moving objects, such as cars, we detect, in human gait, a symmetrical double helical pattern, that can then be analyzed using the Frieze Group theory. The results of tracking on color and thermal sequences demonstrate that our algorithm is robust to illumination noise and performs well in the outdoor environments.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130452970","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}
Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383193
Carlos Hernández, George Vogiatzis, R. Cipolla
We present a new formulation to multi-view stereo that treats the problem as probabilistic 3D segmentation. Previous work has used the stereo photo-consistency criterion as a detector of the boundary between the 3D scene and the surrounding empty space. Here we show how the same criterion can also provide a foreground/background model that can predict if a 3D location is inside or outside the scene. This model replaces the commonly used naive foreground model based on ballooning which is known to perform poorly in concavities. We demonstrate how the probabilistic visibility is linked to previous work on depth-map fusion and we present a multi-resolution graph-cut implementation using the new ballooning term that is very efficient both in terms of computation time and memory requirements.
{"title":"Probabilistic visibility for multi-view stereo","authors":"Carlos Hernández, George Vogiatzis, R. Cipolla","doi":"10.1109/CVPR.2007.383193","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383193","url":null,"abstract":"We present a new formulation to multi-view stereo that treats the problem as probabilistic 3D segmentation. Previous work has used the stereo photo-consistency criterion as a detector of the boundary between the 3D scene and the surrounding empty space. Here we show how the same criterion can also provide a foreground/background model that can predict if a 3D location is inside or outside the scene. This model replaces the commonly used naive foreground model based on ballooning which is known to perform poorly in concavities. We demonstrate how the probabilistic visibility is linked to previous work on depth-map fusion and we present a multi-resolution graph-cut implementation using the new ballooning term that is very efficient both in terms of computation time and memory requirements.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"141 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129175311","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}
Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383062
J. Tardif, A. Bartoli, Martin Trudeau, Nicolas Guilbert, S. Roy
Matrix factorization is a key component for solving several computer vision problems. It is particularly challenging in the presence of missing or erroneous data, which often arise in structure-from-motion. We propose batch algorithms for matrix factorization. They are based on closure and basis constraints, that are used either on the cameras or the structure, leading to four possible algorithms. The constraints are robustly computed from complete measurement sub-matrices with e.g. random data sampling. The cameras and 3D structure are then recovered through linear least squares. Prior information about the scene such as identical camera positions or orientations, smooth camera trajectory, known 3D points and coplanarity of some 3D points can be directly incorporated. We demonstrate our algorithms on challenging image sequences with tracking error and more than 95% missing data.
{"title":"Algorithms for Batch Matrix Factorization with Application to Structure-from-Motion","authors":"J. Tardif, A. Bartoli, Martin Trudeau, Nicolas Guilbert, S. Roy","doi":"10.1109/CVPR.2007.383062","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383062","url":null,"abstract":"Matrix factorization is a key component for solving several computer vision problems. It is particularly challenging in the presence of missing or erroneous data, which often arise in structure-from-motion. We propose batch algorithms for matrix factorization. They are based on closure and basis constraints, that are used either on the cameras or the structure, leading to four possible algorithms. The constraints are robustly computed from complete measurement sub-matrices with e.g. random data sampling. The cameras and 3D structure are then recovered through linear least squares. Prior information about the scene such as identical camera positions or orientations, smooth camera trajectory, known 3D points and coplanarity of some 3D points can be directly incorporated. We demonstrate our algorithms on challenging image sequences with tracking error and more than 95% missing data.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"30 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123206874","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}
Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383298
Liang Wang, D. Suter
We describe a probabilistic framework for recognizing human activities in monocular video based on simple silhouette observations in this paper. The methodology combines kernel principal component analysis (KPCA) based feature extraction and factorial conditional random field (FCRF) based motion modeling. Silhouette data is represented more compactly by nonlinear dimensionality reduction that explores the underlying structure of the articulated action space and preserves explicit temporal orders in projection trajectories of motions. FCRF models temporal sequences in multiple interacting ways, thus increasing joint accuracy by information sharing, with the ideal advantages of discriminative models over generative ones (e.g., relaxing independence assumption between observations and the ability to effectively incorporate both overlapping features and long-range dependencies). The experimental results on two recent datasets have shown that the proposed framework can not only accurately recognize human activities with temporal, intra-and inter-person variations, but also is considerably robust to noise and other factors such as partial occlusion and irregularities in motion styles.
{"title":"Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model","authors":"Liang Wang, D. Suter","doi":"10.1109/CVPR.2007.383298","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383298","url":null,"abstract":"We describe a probabilistic framework for recognizing human activities in monocular video based on simple silhouette observations in this paper. The methodology combines kernel principal component analysis (KPCA) based feature extraction and factorial conditional random field (FCRF) based motion modeling. Silhouette data is represented more compactly by nonlinear dimensionality reduction that explores the underlying structure of the articulated action space and preserves explicit temporal orders in projection trajectories of motions. FCRF models temporal sequences in multiple interacting ways, thus increasing joint accuracy by information sharing, with the ideal advantages of discriminative models over generative ones (e.g., relaxing independence assumption between observations and the ability to effectively incorporate both overlapping features and long-range dependencies). The experimental results on two recent datasets have shown that the proposed framework can not only accurately recognize human activities with temporal, intra-and inter-person variations, but also is considerably robust to noise and other factors such as partial occlusion and irregularities in motion styles.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123322736","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}
Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383064
Yaser Sheikh, Asaad Hakeem, M. Shah
The fundamental matrix is a central construct in the analysis of images captured from a pair of cameras and many feature-based methods have been proposed for its computation. In this paper, we propose a direct method for estimating the fundamental matrix where the motion between the frames is small (e.g. between successive frames of a video). To achieve this, a warping function is presented for the fundamental matrix by using the brightness constancy constraint in conjunction with geometric constraints. Using this warping function, an iterative hierarchical algorithm is described to recover accurate estimates of the fundamental matrix. We present results of experimentation to evaluate the performance of the proposed approach and demonstrate improved accuracy in the computation of the fundamental matrix.
{"title":"On the Direct Estimation of the Fundamental Matrix","authors":"Yaser Sheikh, Asaad Hakeem, M. Shah","doi":"10.1109/CVPR.2007.383064","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383064","url":null,"abstract":"The fundamental matrix is a central construct in the analysis of images captured from a pair of cameras and many feature-based methods have been proposed for its computation. In this paper, we propose a direct method for estimating the fundamental matrix where the motion between the frames is small (e.g. between successive frames of a video). To achieve this, a warping function is presented for the fundamental matrix by using the brightness constancy constraint in conjunction with geometric constraints. Using this warping function, an iterative hierarchical algorithm is described to recover accurate estimates of the fundamental matrix. We present results of experimentation to evaluate the performance of the proposed approach and demonstrate improved accuracy in the computation of the fundamental matrix.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123362971","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}
Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383492
Andrew C. Gallagher, Tsuhan Chen
While face recognition techniques have rapidly advanced in the last few years, most of the work is in the domain of security applications. For consumer imaging applications, person recognition is an important tool that is useful for searching and retrieving images from a personal image collection. It has been shown that when recognizing a single person in an image, a maximum likelihood classifier requires the prior probability for each candidate individual. In this paper, we extend this idea and describe the benefits of using a group prior for identifying people in consumer images with multiple people. The group prior describes the probability of a group of individuals appearing together in an image. In our application, we have a subset of ambiguously labeled images for a consumer image collection, where we seek to identify all of the people in the collection. We describe a simple algorithm for resolving the ambiguous labels. We show that despite errors in resolving ambiguous labels, useful classifiers can be trained with the resolved labels. Recognition performance is further improved with a group prior learned from the ambiguous labels. In summary, by modeling the relationships between the people with the group prior, we improve classification performance.
{"title":"Using Group Prior to Identify People in Consumer Images","authors":"Andrew C. Gallagher, Tsuhan Chen","doi":"10.1109/CVPR.2007.383492","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383492","url":null,"abstract":"While face recognition techniques have rapidly advanced in the last few years, most of the work is in the domain of security applications. For consumer imaging applications, person recognition is an important tool that is useful for searching and retrieving images from a personal image collection. It has been shown that when recognizing a single person in an image, a maximum likelihood classifier requires the prior probability for each candidate individual. In this paper, we extend this idea and describe the benefits of using a group prior for identifying people in consumer images with multiple people. The group prior describes the probability of a group of individuals appearing together in an image. In our application, we have a subset of ambiguously labeled images for a consumer image collection, where we seek to identify all of the people in the collection. We describe a simple algorithm for resolving the ambiguous labels. We show that despite errors in resolving ambiguous labels, useful classifiers can be trained with the resolved labels. Recognition performance is further improved with a group prior learned from the ambiguous labels. In summary, by modeling the relationships between the people with the group prior, we improve classification performance.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126212676","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}
Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383533
K. Kalgaonkar, P. Smaragdis, B. Raj
In this chapter we present two technologies for sensing and surveillance -audio-assisted cameras and acoustic Doppler sensors for gait recognition.
在本章中，我们介绍了两种用于传感和监视的技术——音频辅助摄像机和用于步态识别的声学多普勒传感器。
{"title":"Sensor and Data Systems, Audio-Assisted Cameras and Acoustic Doppler Sensors","authors":"K. Kalgaonkar, P. Smaragdis, B. Raj","doi":"10.1109/CVPR.2007.383533","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383533","url":null,"abstract":"In this chapter we present two technologies for sensing and surveillance -audio-assisted cameras and acoustic Doppler sensors for gait recognition.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126376650","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}
Pub Date : 2007-06-17DOI: 10.1109/CVPR.2007.383383
Zhi Zhang, Vartika Singh, T. E. Slowe, S. Tulyakov, V. Govindaraju
Being the most broadly used tool for deceit measurement, the polygraph is a limited method as it suffers from human operator subjectivity and the fact that target subjects are aware of the measurement, which invites the opportunity to alter their behavior or plan counter-measures in advance. The approach presented in this paper attempts to circumvent these problems by unobtrusively and automatically measuring several prior identified deceit indicators (DIs) based upon involuntary, so-called reliable facial expressions through computer vision analysis of image sequences in real time. Reliable expressions are expressions said by the psychology community to be impossible for a significant percentage of the population to convincingly simulate, without feeling a true inner felt emotion. The strategy is to detect the difference between those expressions which arise from internal emotion, implying verity, and those expressions which are simulated, implying deceit. First, a group of facial action units (AUs) related to the reliable expressions are detected based on distance and texture based features. The DIs then can be measured and finally a decision of deceit or verity will be made accordingly. The performance of this proposed approach is evaluated by its real time implementation for deceit detection.
{"title":"Real-time Automatic Deceit Detection from Involuntary Facial Expressions","authors":"Zhi Zhang, Vartika Singh, T. E. Slowe, S. Tulyakov, V. Govindaraju","doi":"10.1109/CVPR.2007.383383","DOIUrl":"https://doi.org/10.1109/CVPR.2007.383383","url":null,"abstract":"Being the most broadly used tool for deceit measurement, the polygraph is a limited method as it suffers from human operator subjectivity and the fact that target subjects are aware of the measurement, which invites the opportunity to alter their behavior or plan counter-measures in advance. The approach presented in this paper attempts to circumvent these problems by unobtrusively and automatically measuring several prior identified deceit indicators (DIs) based upon involuntary, so-called reliable facial expressions through computer vision analysis of image sequences in real time. Reliable expressions are expressions said by the psychology community to be impossible for a significant percentage of the population to convincingly simulate, without feeling a true inner felt emotion. The strategy is to detect the difference between those expressions which arise from internal emotion, implying verity, and those expressions which are simulated, implying deceit. First, a group of facial action units (AUs) related to the reliable expressions are detected based on distance and texture based features. The DIs then can be measured and finally a decision of deceit or verity will be made accordingly. The performance of this proposed approach is evaluated by its real time implementation for deceit detection.","PeriodicalId":351008,"journal":{"name":"2007 IEEE Conference on Computer Vision and Pattern Recognition","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126090416","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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