8 research outputs found
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Background suppressing Gabor energy filtering
In the field of facial emotion recognition, early research advanced with the use of Gabor filters. However, these filters lack generalization and result in undesirably large feature vector size. In recent work, more attention has been given to other local appearance features. Two desired characteristics in a facial appearance feature are generalization capability, and the compactness of representation. In this paper, we propose a novel texture feature inspired by Gabor energy filters, called background suppressing Gabor energy filtering. The feature has a generalization component that removes background texture. It has a reduced feature vector size due to maximal representation and soft orientation histograms, and it is awhite box representation. We demonstrate improved performance on the non-trivial Audio/Visual Emotion Challenge 2012 grand-challenge dataset by a factor of 7.17 over the Gabor filter on the development set. We also demonstrate applicability of our approach beyond facial emotion recognition which yields improved classification rate over the Gabor filter for four bioimaging datasets by an average of 8.22%
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Modeling uncertainties in performance of object recognition
Efficient probability modeling is indispensable for uncertainty quantification of the recognition data. If the model assumptions do not reflect the intrinsic nature of data and associated random variables, then a strong performance measure will most likely fail to come up with a correct match for recognition. In this paper we propose the probability models for two kinds of data obtained with two distinct goals of recognition: identification and discovery. We consider both frequentisi and Bayesian approaches for drawing inferences from the data
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Predictive models for multibiometric systems
Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations
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Structural signatures for passenger vehicle classification in video
This paper focuses on a challenging pattern recognition problem of significant industrial impact, i.e., classifying vehicles from their rear videos as observed by a camera mounted on top of a highway with vehicles traveling at high speed. To solve this problem, this paper presents a novel feature called structural signature. From a rear-view video, a structural signature recovers the vehicle side profile information, which is crucial in its classification. As a vehicle moves away from a camera, its surfaces deform differently based on their relative orientation to the camera. This information is used to extract the structure of a vehicle, which captures the relative orientation of vehicle surfaces and the road surface. This paper presents a complete system that computes structural signatures and uses them for classification of passenger vehicles into sedans, pickups, and minivans/sport utility vehicles in highway videos. It analyzes the performance of the proposed system on a large video data set. © 2013 IEEE
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Vision and attention theory based sampling for continuous facial emotion recognition
Affective computing - the emergent field in which computers detect emotions and project appropriate expressions of their own - has reached a bottleneck where algorithms are not able to infer a person's emotions from natural and spontaneous facial expressions captured in video. While the field of emotion recognition has seen many advances in the past decade, a facial emotion recognition approach has not yet been revealed which performs well in unconstrained settings. In this paper, we propose a principled method which addresses the temporal dynamics of facial emotions and expressions in video with a sampling approach inspired from human perceptual psychology. We test the efficacy of the method on the Audio/Visual Emotion Challenge 2011 and 2012, Cohn-Kanade and the MMI Facial Expression Database. The method shows an average improvement of 9.8 percent over the baseline for weighted accuracy on the Audio/Visual Emotion Challenge 2011 video-based frame-level subchallenge testing set
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Learning small gallery size for prediction of recognition performance on large populations
This paper addresses the estimation of a small gallery size that can generate the optimal error estimate and its confidence on a large population (relative to the size of the gallery) which is one of the fundamental problems encountered in performance prediction for object recognition. It uses a generalized two-dimensional prediction model that combines a hypergeometric probability distribution model with a binomial model and also considers the data distortion problem in large populations. Learning is incorporated in the prediction process in order to find the optimal small gallery size and to improve the prediction. The Chernoff and Chebychev inequalities are used as a guide to obtain the small gallery size. During the prediction, the expectation-maximization (EM) algorithm is used to learn the match score and the non-match score distributions that are represented as a mixture of Gaussians. The optimal size of the small gallery is learned by comparing it with the sizes obtained by the statistical approaches and at the same time the upper and lower bounds for the prediction on large populations are obtained. Results for the prediction are presented for the NIST-4 fingerprint database. © 2013 Elsevier Ltd
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Background suppressing Gabor energy filtering
In the field of facial emotion recognition, early research advanced with the use of Gabor filters. However, these filters lack generalization and result in undesirably large feature vector size. In recent work, more attention has been given to other local appearance features. Two desired characteristics in a facial appearance feature are generalization capability, and the compactness of representation. In this paper, we propose a novel texture feature inspired by Gabor energy filters, called background suppressing Gabor energy filtering. The feature has a generalization component that removes background texture. It has a reduced feature vector size due to maximal representation and soft orientation histograms, and it is awhite box representation. We demonstrate improved performance on the non-trivial Audio/Visual Emotion Challenge 2012 grand-challenge dataset by a factor of 7.17 over the Gabor filter on the development set. We also demonstrate applicability of our approach beyond facial emotion recognition which yields improved classification rate over the Gabor filter for four bioimaging datasets by an average of 8.22%
Recommended from our members
Modeling uncertainties in performance of object recognition
Efficient probability modeling is indispensable for uncertainty quantification of the recognition data. If the model assumptions do not reflect the intrinsic nature of data and associated random variables, then a strong performance measure will most likely fail to come up with a correct match for recognition. In this paper we propose the probability models for two kinds of data obtained with two distinct goals of recognition: identification and discovery. We consider both frequentisi and Bayesian approaches for drawing inferences from the data