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This paper presents a method for face identification under adverse conditions by combining regular, frontal face images with facial strain maps using score-level fusion. Strain maps are generated by calculating the central difference method of the optical flow field obtained from each subject's face during the open mouth expression. Subjects were recorded with and without camouflage under three lighting...
This paper presents a novel method for automatic spotting (temporal segmentation) of facial expressions in long videos comprising of continuous and changing expressions. The method utilizes the strain impacted on the facial skin due to the non-rigid motion caused during expressions. The strain magnitude is calculated using the central difference method over the robust and dense optical flow field...
We present a system to retrieve all clips from a meeting archive that show a particular individual speaking, using a single face or voice sample as the query. The system incorporates three novel ideas. One, rather than match the query to each individual sample in the archive, samples within a meeting are grouped first, generating a cluster of samples per individual. The query is then matched to the...
We present a finite element modeling based approach to compute strain patterns caused by facial deformation during expressions in videos. A sparse motion field computed through a robust optical flow method drives the FE model. While the geometry of the model is generic, the material constants associated with an individualpsilas facial skin are learned at a coarse level sufficient for accurate strain...
We consider if it is possible to embed biometric similarity scores in low-dimensional, inner product spaces. Starting from a matrix of similarity scores between target and query templates, as computed by a biometric algorithm, we seek to represent the templates as points in a low-dimensional space such that their inner products approximates the neighborhood relationship in the given similarity matrix...
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