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Facial activity is the most direct signal for perceiving emotional states in people. Emotion analysis from facial displays has been attracted an increasing attention because of its wide applications from human-centered computing to neuro-psychiatry. Recently, image representation based on sparse coding has shown promising results in facial expression recognition. In this paper, we introduce a novel...
Multi-modal biometric fusion is more accurate and reliable compared to recognition using a single biometric modality. However, most existing fusion approaches neglect the influence of the qualities of the biometric samples in information fusion. Our goal is to advance the state-of-the-art in biometric fusion technology by providing a more universal and more accurate solution for personal identification...
Joint alignment for an image ensemble can rectify images in the spatial domain such that the aligned images are as similar to each other as possible. This important technology has been applied to various object classes and medical applications. However, previous approaches to joint alignment work on an ensemble of a single object class. Given an ensemble with multiple object classes, we propose an...
This paper addresses the problem of developing facial image quality metrics that are predictive of the performance of existing biometric matching algorithms and incorporating the quality estimates into the recognition decision process to improve overall performance. The first task we consider is the separation of probe/gallery qualities since the match score depends on both. Given a set of training...
For many computer vision problems, it is very important to produce the ground truth data. Manual data labeling is labor-intensive and prone to the human errors, whereas fully automatic data labeling is not feasible and reliable. In this paper, we propose an interactive labeling technique for efficient and accurate data labeling. Constructed on a Bayesian network (BN), the automatic image labeler produces...
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