Steven Cadavid - Academia.edu (original) (raw)
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Papers by Steven Cadavid
2010 20th International Conference on Pattern Recognition, 2010
2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007
Personal and Ubiquitous Computing, 2012
Steady increases in healthcare costs and obesity have inspired recent studies into cost-effective... more Steady increases in healthcare costs and obesity have inspired recent studies into cost-effective, assistive systems capable of monitoring dietary habits. Few researchers, though, have investigated the use of video as a means of monitoring dietary activities. Video possesses several inherent qualities, such as passive acquisition, that merits its analysis as an input modality for such an application. To this end, we propose a method to automatically detect chewing events in surveillance video of a subject. Firstly, an Active Appearance Model (AAM) is used to track a subject’s face across the video sequence. It is observed that the variations in the AAM parameters across chewing events demonstrate a distinct periodicity. We utilize this property to discriminate between chewing and non-chewing facial actions such as talking. A feature representation is constructed by applying spectral analysis to a temporal window of model parameter values. The estimated power spectra subsequently undergo non-linear dimensionality reduction. The low-dimensional embedding of the power spectra are employed to train a binary Support Vector Machine classifier to detect chewing events. To emulate the gradual onset and offset of chewing, smoothness is imposed over the class predictions of neighboring video frames in order to deter abrupt changes in the class labels. Experiments are conducted on a dataset consisting of 37 subjects performing each of five actions, namely, open- and closed-mouth chewing, clutter faces, talking, and still face. Experimental results yielded a cross-validated percentage agreement of 93.0%, indicating that the proposed system provides an efficient approach to automated chewing detection.
IEEE Transactions on Information Forensics and Security, 2000
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009
2009 IEEE International Workshop on Safety, Security & Rescue Robotics (SSRR 2009), 2009
2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009
Procedings of the British Machine Vision Conference 2009, 2009
CVPR 2011 WORKSHOPS, 2011
2009 16th IEEE International Conference on Image Processing (ICIP), 2009
2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2012
ABSTRACT In this paper, we present a fully automated approach for ear recognition based upon spar... more ABSTRACT In this paper, we present a fully automated approach for ear recognition based upon sparse representation. In sparse representation, features extracted from the training data of each subject are used to develop a dictionary. In this work, Gabor filters are used for feature extraction. Classification is performed by extracting features from the test data and using the dictionary for representing the test data. The class of the test data is then determined based upon the involvement of the dictionary entries in its representation. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing large appearance, pose, and lighting variability, yielded a rank-one recognition rate of 98.46%. The proposed system outperforms the method described in [1], which achieves a recognition rate of 96.88% when evaluated on the same dataset. Moreover, the proposed system was evaluated on a greater number of test images per subject, demonstrating its robustness.
2011 18th IEEE International Conference on Image Processing, 2011
Social Emotions in Nature and Artifact, 2013
Multibiometrics for Human Identification, 2009
2008 7th IEEE International Conference on Development and Learning, 2008
IEEE Transactions on Information Forensics and Security, 2000
2010 20th International Conference on Pattern Recognition, 2010
2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems, 2007
Personal and Ubiquitous Computing, 2012
Steady increases in healthcare costs and obesity have inspired recent studies into cost-effective... more Steady increases in healthcare costs and obesity have inspired recent studies into cost-effective, assistive systems capable of monitoring dietary habits. Few researchers, though, have investigated the use of video as a means of monitoring dietary activities. Video possesses several inherent qualities, such as passive acquisition, that merits its analysis as an input modality for such an application. To this end, we propose a method to automatically detect chewing events in surveillance video of a subject. Firstly, an Active Appearance Model (AAM) is used to track a subject’s face across the video sequence. It is observed that the variations in the AAM parameters across chewing events demonstrate a distinct periodicity. We utilize this property to discriminate between chewing and non-chewing facial actions such as talking. A feature representation is constructed by applying spectral analysis to a temporal window of model parameter values. The estimated power spectra subsequently undergo non-linear dimensionality reduction. The low-dimensional embedding of the power spectra are employed to train a binary Support Vector Machine classifier to detect chewing events. To emulate the gradual onset and offset of chewing, smoothness is imposed over the class predictions of neighboring video frames in order to deter abrupt changes in the class labels. Experiments are conducted on a dataset consisting of 37 subjects performing each of five actions, namely, open- and closed-mouth chewing, clutter faces, talking, and still face. Experimental results yielded a cross-validated percentage agreement of 93.0%, indicating that the proposed system provides an efficient approach to automated chewing detection.
IEEE Transactions on Information Forensics and Security, 2000
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009
2009 IEEE International Workshop on Safety, Security & Rescue Robotics (SSRR 2009), 2009
2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, 2009
Procedings of the British Machine Vision Conference 2009, 2009
CVPR 2011 WORKSHOPS, 2011
2009 16th IEEE International Conference on Image Processing (ICIP), 2009
2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2012
ABSTRACT In this paper, we present a fully automated approach for ear recognition based upon spar... more ABSTRACT In this paper, we present a fully automated approach for ear recognition based upon sparse representation. In sparse representation, features extracted from the training data of each subject are used to develop a dictionary. In this work, Gabor filters are used for feature extraction. Classification is performed by extracting features from the test data and using the dictionary for representing the test data. The class of the test data is then determined based upon the involvement of the dictionary entries in its representation. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing large appearance, pose, and lighting variability, yielded a rank-one recognition rate of 98.46%. The proposed system outperforms the method described in [1], which achieves a recognition rate of 96.88% when evaluated on the same dataset. Moreover, the proposed system was evaluated on a greater number of test images per subject, demonstrating its robustness.
2011 18th IEEE International Conference on Image Processing, 2011
Social Emotions in Nature and Artifact, 2013
Multibiometrics for Human Identification, 2009
2008 7th IEEE International Conference on Development and Learning, 2008
IEEE Transactions on Information Forensics and Security, 2000