Pub Date : 2008-11-05DOI: 10.1109/MMSP.2008.4665206
Te Li, S. Rahardja, S. Koh
The bit allocation algorithm for stereo channels in MPEG-4 scalable lossless coding (SLS) is not optimized. A perceptually enhanced stereo bit allocation algorithm for fully scalable audio coding is presented in this paper. According to the energy distribution in different channels, the bitrate is allocated in a much more efficient manner. Experiment results show that the proposed method significantly improves the perceptual quality of the fully scalable audio at various bitrates without introducing any new side information.
{"title":"Efficient stereo bitrate allocation for fully scalable audio codec","authors":"Te Li, S. Rahardja, S. Koh","doi":"10.1109/MMSP.2008.4665206","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665206","url":null,"abstract":"The bit allocation algorithm for stereo channels in MPEG-4 scalable lossless coding (SLS) is not optimized. A perceptually enhanced stereo bit allocation algorithm for fully scalable audio coding is presented in this paper. According to the energy distribution in different channels, the bitrate is allocated in a much more efficient manner. Experiment results show that the proposed method significantly improves the perceptual quality of the fully scalable audio at various bitrates without introducing any new side information.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127341299","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 : 2008-11-05DOI: 10.1109/MMSP.2008.4665188
Bei Na Wei, Yu Shi, G. Ye, Jie Xu
Smart camera system design and implementation is a challenging task due to the constant need to perform computationally demanding image processing tasks with the limited resource constraints of embedded systems. This paper presents the hardware and software co-design and implementation of the first stage of TraffiCam, an FPGA based smart camera prototype for traffic surveillance at intersections, consisting of a CMOS image sensor capture device and FPGA main video processor. In particular, creative solutions for balancing gate array utilization, memory and computation time are presented for the initial stage of Harris keypoint detection with discussions on the algorithm implementation conversions between PC-based to FPGA based platforms. Preliminary results show satisfactory real-time tracking and estimation performance.
{"title":"Developing a smart camera for road traffic surveillance","authors":"Bei Na Wei, Yu Shi, G. Ye, Jie Xu","doi":"10.1109/MMSP.2008.4665188","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665188","url":null,"abstract":"Smart camera system design and implementation is a challenging task due to the constant need to perform computationally demanding image processing tasks with the limited resource constraints of embedded systems. This paper presents the hardware and software co-design and implementation of the first stage of TraffiCam, an FPGA based smart camera prototype for traffic surveillance at intersections, consisting of a CMOS image sensor capture device and FPGA main video processor. In particular, creative solutions for balancing gate array utilization, memory and computation time are presented for the initial stage of Harris keypoint detection with discussions on the algorithm implementation conversions between PC-based to FPGA based platforms. Preliminary results show satisfactory real-time tracking and estimation performance.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116644709","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 : 2008-11-05DOI: 10.1109/MMSP.2008.4665141
M. Awrangjeb, Guojun Lu
The SIFT (scale invariant feature transform) has demonstrated its superior performance in identifying transformed images over many other approaches. However, both of its detection and matching stages are expensive, because a large number of keypoints are detected in the scale-space and each keypoint is described using a 128-dimensional vector. We present two possible solutions for feature-point reduction. First is to down scale the image before the SIFT keypoint detection and second is to use corners (instead of SIFT keypoints) which are visually significant, more robust, and much smaller in number than the SIFT keypoints. Either the curvature descriptor or the highly distinctive SIFT descriptors at corner locations can be used to represent corners.We then describe a new feature-point matching technique, which can be used for matching both the down-scaled SIFT keypoints and corners. Experimental results show that two feature-point reduction solutions combined with the SIFT descriptors and the proposed feature-point matching technique not only improve the computational efficiency and decrease the storage requirement, but also improve the transformed image identification accuracy (robustness).
{"title":"Efficient and effective transformed image identification","authors":"M. Awrangjeb, Guojun Lu","doi":"10.1109/MMSP.2008.4665141","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665141","url":null,"abstract":"The SIFT (scale invariant feature transform) has demonstrated its superior performance in identifying transformed images over many other approaches. However, both of its detection and matching stages are expensive, because a large number of keypoints are detected in the scale-space and each keypoint is described using a 128-dimensional vector. We present two possible solutions for feature-point reduction. First is to down scale the image before the SIFT keypoint detection and second is to use corners (instead of SIFT keypoints) which are visually significant, more robust, and much smaller in number than the SIFT keypoints. Either the curvature descriptor or the highly distinctive SIFT descriptors at corner locations can be used to represent corners.We then describe a new feature-point matching technique, which can be used for matching both the down-scaled SIFT keypoints and corners. Experimental results show that two feature-point reduction solutions combined with the SIFT descriptors and the proposed feature-point matching technique not only improve the computational efficiency and decrease the storage requirement, but also improve the transformed image identification accuracy (robustness).","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132234830","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 : 2008-11-05DOI: 10.1109/MMSP.2008.4665145
G. Herman, G. Ye, Jie Xu, Bang Zhang
In this paper we propose a new algorithm for region-based image categorization that is formulated as a multiple instance learning (MIL) problem. The proposed algorithm transforms the MIL problem into a traditional supervised learning problem, and solves it using a standard supervised learning method. The features used in the proposed algorithm are the hyperclique patterns which are ldquocondensedrdquo into a small set of discriminative features. Each hyperclique pattern consists of multiple strongly-correlated instances (i.e., features). As a result, hyperclique patterns are able to capture the information that are not shared by individual features. The advantages of the proposed algorithm over existing algorithms are threefold: (i) unlike some existing algorithms which use learning methods that are specifically designed for MIL or for certain datasets, the proposed algorithm uses a general-purpose standard supervised learning method, (ii) it uses a significantly small set of features which are empirically more discriminative than the PCA features (i.e. principal components), and (iii) it is simple and efficient and achieves a comparable performance to most state-of-the-art algorithms. The efficiency and good performance of the proposed algorithm make it a practical solution to general MIL problems. In this paper, we apply the proposed algorithm to both drug activity prediction and image categorization, and promising results are obtained.
{"title":"Region-based image categorization with reduced feature set","authors":"G. Herman, G. Ye, Jie Xu, Bang Zhang","doi":"10.1109/MMSP.2008.4665145","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665145","url":null,"abstract":"In this paper we propose a new algorithm for region-based image categorization that is formulated as a multiple instance learning (MIL) problem. The proposed algorithm transforms the MIL problem into a traditional supervised learning problem, and solves it using a standard supervised learning method. The features used in the proposed algorithm are the hyperclique patterns which are ldquocondensedrdquo into a small set of discriminative features. Each hyperclique pattern consists of multiple strongly-correlated instances (i.e., features). As a result, hyperclique patterns are able to capture the information that are not shared by individual features. The advantages of the proposed algorithm over existing algorithms are threefold: (i) unlike some existing algorithms which use learning methods that are specifically designed for MIL or for certain datasets, the proposed algorithm uses a general-purpose standard supervised learning method, (ii) it uses a significantly small set of features which are empirically more discriminative than the PCA features (i.e. principal components), and (iii) it is simple and efficient and achieves a comparable performance to most state-of-the-art algorithms. The efficiency and good performance of the proposed algorithm make it a practical solution to general MIL problems. In this paper, we apply the proposed algorithm to both drug activity prediction and image categorization, and promising results are obtained.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133243872","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 : 2008-11-05DOI: 10.1109/MMSP.2008.4665214
Kyu Jeong Han, P. Georgiou, Shrikanth S. Narayanan
In this paper, we propose a novel approach to speaker diarization of spontaneous meetings in our own multimodal SmartRoom environment. The proposed speaker diarization system first applies a sequential clustering concept to segmentation of a given audio data source, and then performs agglomerative hierarchical clustering for speaker-specific classification (or speaker clustering) of speech segments. The speaker clustering algorithm utilizes an incremental Gaussian mixture cluster modeling strategy, and a stopping point estimation method based on information change rate. Through experiments on various meeting conversation data of approximately 200 minutes total length, this system is demonstrated to provide diarization error rate of 18.90% on average.
{"title":"The SAIL speaker diarization system for analysis of spontaneous meetings","authors":"Kyu Jeong Han, P. Georgiou, Shrikanth S. Narayanan","doi":"10.1109/MMSP.2008.4665214","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665214","url":null,"abstract":"In this paper, we propose a novel approach to speaker diarization of spontaneous meetings in our own multimodal SmartRoom environment. The proposed speaker diarization system first applies a sequential clustering concept to segmentation of a given audio data source, and then performs agglomerative hierarchical clustering for speaker-specific classification (or speaker clustering) of speech segments. The speaker clustering algorithm utilizes an incremental Gaussian mixture cluster modeling strategy, and a stopping point estimation method based on information change rate. Through experiments on various meeting conversation data of approximately 200 minutes total length, this system is demonstrated to provide diarization error rate of 18.90% on average.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133391583","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 : 2008-11-05DOI: 10.1109/MMSP.2008.4665070
W. Li, Zhengyou Zhang, Zicheng Liu
This paper presents a graphical model for learning and recognizing human actions. Specifically, we propose to encode actions in a weighted directed graph, referred to as action graph, where nodes of the graph represent salient postures that are used to characterize the actions and shared by all actions. The weight between two nodes measures the transitional probability between the two postures. An action is encoded as one or multiple paths in the action graph. The salient postures are modeled using Gaussian mixture models (GMM). Both the salient postures and action graph are automatically learned from training samples through unsupervised clustering and expectation and maximization (EM) algorithm. Experimental results have verified the performance of the proposed model, its tolerance to noise and viewpoints and its robustness across different subjects and datasets.
{"title":"Graphical modeling and decoding of human actions","authors":"W. Li, Zhengyou Zhang, Zicheng Liu","doi":"10.1109/MMSP.2008.4665070","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665070","url":null,"abstract":"This paper presents a graphical model for learning and recognizing human actions. Specifically, we propose to encode actions in a weighted directed graph, referred to as action graph, where nodes of the graph represent salient postures that are used to characterize the actions and shared by all actions. The weight between two nodes measures the transitional probability between the two postures. An action is encoded as one or multiple paths in the action graph. The salient postures are modeled using Gaussian mixture models (GMM). Both the salient postures and action graph are automatically learned from training samples through unsupervised clustering and expectation and maximization (EM) algorithm. Experimental results have verified the performance of the proposed model, its tolerance to noise and viewpoints and its robustness across different subjects and datasets.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122531784","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 : 2008-11-05DOI: 10.1109/MMSP.2008.4665117
Jingyuan Wang, Lifeng Sun, Bin Li, Meng Zhang, Shiqiang Yang
In this paper, we propose a novel scheme using computing complexity layered scalable video coding (CCLSVC) to optimize the user experience of broadcasting video in the computing capability limited handheld terminals. To address the heterogeneity of computing capability among different handheld devices, we employ hierarchal B reference structure of SVC to divide the frames into multiple computing complexity layers (CC Layers) in server side. The handheld clients simply choose to decode the frames in their corresponding layers in terms of their computation capability to maximize the video PSNR. We have proved that the optimal CC Layers division problem is a precedence constrained scheduling problem, which is an NP-complete problem. And we further propose our fast greedy method to approximately get optimized broadcasting video playback PSNR. The simulation shows that our method is superior to temporal SVC and random frame discarding method.
{"title":"CCL-SVC: Optimizing user experience of broadcasting video on computation capability limited handheld devices","authors":"Jingyuan Wang, Lifeng Sun, Bin Li, Meng Zhang, Shiqiang Yang","doi":"10.1109/MMSP.2008.4665117","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665117","url":null,"abstract":"In this paper, we propose a novel scheme using computing complexity layered scalable video coding (CCLSVC) to optimize the user experience of broadcasting video in the computing capability limited handheld terminals. To address the heterogeneity of computing capability among different handheld devices, we employ hierarchal B reference structure of SVC to divide the frames into multiple computing complexity layers (CC Layers) in server side. The handheld clients simply choose to decode the frames in their corresponding layers in terms of their computation capability to maximize the video PSNR. We have proved that the optimal CC Layers division problem is a precedence constrained scheduling problem, which is an NP-complete problem. And we further propose our fast greedy method to approximately get optimized broadcasting video playback PSNR. The simulation shows that our method is superior to temporal SVC and random frame discarding method.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123861336","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 : 2008-11-05DOI: 10.1109/MMSP.2008.4665126
G. Rath, C. Guillemot, J. Fuchs
This paper considers the application of sparse approximations in a joint source-channel (JSC) coding framework. The considered JSC coded system employs a real number BCH code on the input signal before the signal is quantized and further processed. Under an impulse channel noise model, the decoding of error is posed as a sparse approximation problem. The orthogonal matching pursuit (OMP) and basis pursuit (BP) algorithms are compared with the syndrome decoding algorithm in terms of mean square reconstruction error. It is seen that, with a Gauss-Markov source and Bernoulli-Gaussian channel noise, the BP outperforms the syndrome decoding and the OMP at higher noise levels. In the case of image transmission with channel bit errors, the BP outperforms the other two decoding algorithms consistently.
{"title":"Sparse approximations for joint source-channel coding","authors":"G. Rath, C. Guillemot, J. Fuchs","doi":"10.1109/MMSP.2008.4665126","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665126","url":null,"abstract":"This paper considers the application of sparse approximations in a joint source-channel (JSC) coding framework. The considered JSC coded system employs a real number BCH code on the input signal before the signal is quantized and further processed. Under an impulse channel noise model, the decoding of error is posed as a sparse approximation problem. The orthogonal matching pursuit (OMP) and basis pursuit (BP) algorithms are compared with the syndrome decoding algorithm in terms of mean square reconstruction error. It is seen that, with a Gauss-Markov source and Bernoulli-Gaussian channel noise, the BP outperforms the syndrome decoding and the OMP at higher noise levels. In the case of image transmission with channel bit errors, the BP outperforms the other two decoding algorithms consistently.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123996434","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 : 2008-11-05DOI: 10.1109/MMSP.2008.4665048
Samir-Mohamad Omar, D. Slock
We consider the blind multichannel dereverberation problem for a single source. We have shown before [5] that the single-input multi-output (SIMO) reverberation filter can be equalized blindly by applying MIMO Linear Prediction (LP) to its output (after SISO input pre-whitening). In this paper, we investigate the LP-based dereverberation in a noisy environment, and/or under acoustic channel length underestimation. Considering ambient noise and late reverberation as additive noises, we propose to introduce a postfilter that transforms the MIMO prediction filter into a somewhat longer equalizer. The postfilter allows to equalize to non-zero delay. Both MMSE-ZF and MMSE design criteria are considered here for the postfilter.We also focus here on computationally efficient (FFT based) block Toeplitz covariance matrix enhancement that enforces the SIMO filtered source plus white noise structure before applying MIMO LP. A second suggested refinement is an iterative refinement between SISO and MIMO LP. Simulations show that the proposed scheme is robust in noisy environments, and performs better compared to the classic Delay-&-Predict equalizer and the Delay-&-Sum beamformer.
{"title":"Singular block Toeplitz matrix approximation and application to multi-microphone speech dereverberation","authors":"Samir-Mohamad Omar, D. Slock","doi":"10.1109/MMSP.2008.4665048","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665048","url":null,"abstract":"We consider the blind multichannel dereverberation problem for a single source. We have shown before [5] that the single-input multi-output (SIMO) reverberation filter can be equalized blindly by applying MIMO Linear Prediction (LP) to its output (after SISO input pre-whitening). In this paper, we investigate the LP-based dereverberation in a noisy environment, and/or under acoustic channel length underestimation. Considering ambient noise and late reverberation as additive noises, we propose to introduce a postfilter that transforms the MIMO prediction filter into a somewhat longer equalizer. The postfilter allows to equalize to non-zero delay. Both MMSE-ZF and MMSE design criteria are considered here for the postfilter.We also focus here on computationally efficient (FFT based) block Toeplitz covariance matrix enhancement that enforces the SIMO filtered source plus white noise structure before applying MIMO LP. A second suggested refinement is an iterative refinement between SISO and MIMO LP. Simulations show that the proposed scheme is robust in noisy environments, and performs better compared to the classic Delay-&-Predict equalizer and the Delay-&-Sum beamformer.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116736593","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 : 2008-11-05DOI: 10.1109/MMSP.2008.4665158
Pohsiang Tsai, Tich Phuoc Tran, T. Hintz, T. Jan
Physiological and/or behavioural characteristics of humans such as face, gait and/or voice have been used in biometric recognition technology. Apart from these characteristics (which have been reported in the literature), the hypothesis of this research was to investigate if facial behaviour could be used for human identification. We analysed and proposed a multiple experts system, called Adaptive Multiple Experts System (AMES), for validating our hypothesis and analysis. We used the Japanese Female Facial Expression (JAFFE) database as it provides the facial behaviour traits for data collection. The experimental results indicate that facial behaviours may provide information about individual difference and, thus may be used as another behavioural biometric.
{"title":"Adaptive Multiple Experts System for personal identification using facial behaviour biometrics","authors":"Pohsiang Tsai, Tich Phuoc Tran, T. Hintz, T. Jan","doi":"10.1109/MMSP.2008.4665158","DOIUrl":"https://doi.org/10.1109/MMSP.2008.4665158","url":null,"abstract":"Physiological and/or behavioural characteristics of humans such as face, gait and/or voice have been used in biometric recognition technology. Apart from these characteristics (which have been reported in the literature), the hypothesis of this research was to investigate if facial behaviour could be used for human identification. We analysed and proposed a multiple experts system, called Adaptive Multiple Experts System (AMES), for validating our hypothesis and analysis. We used the Japanese Female Facial Expression (JAFFE) database as it provides the facial behaviour traits for data collection. The experimental results indicate that facial behaviours may provide information about individual difference and, thus may be used as another behavioural biometric.","PeriodicalId":402287,"journal":{"name":"2008 IEEE 10th Workshop on Multimedia Signal Processing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115201708","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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