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In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class-specific galleries. The inverse(More)
In this paper we address the problem of illumination invariant face recognition. Using a fundamental concept that in general, patterns from a single object class lie on a linear subspace [2], we develop a linear model representing a probe image as a linear combination of class-specific galleries. In the presence of noise, the well-conditioned inverse(More)
We address the closed-set problem of speaker identification by presenting a novel sparse representation classification algorithm. We propose to develop an overcomplete dictionary using the GMM mean super-vector kernel for all the training utterances. A given test utterance corresponds to only a small fraction of the whole training database. We therefore(More)
We propose in this paper a model based technique for the detection of human faces from rich still color images. Traditionally, color images are represented in the RGB color space. RGB space, however, is not only a 3-dimensional space but also includes brightness or luminance which is not a reliable criterion for skin separation. To avoid the effect of(More)
This paper presents an audio visual (AV) person identification system using Linear Regression-based Classifier (LRC) for person identification. Class specific models are created by stacking q-dimensional speech and image vectors from the training data. The person identification task is considered a linear regression problem, i.e., a test (speech or image)(More)
In this paper we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class specific galleries. The inverse(More)
In this paper, speaker identification using the Dempster-Shafer theory of evidence is discussed. The objective is to use the complementary information present from different clas-sifiers to fuse the classification results into a single decision. Here, we use a decreasing function of the distance (of the classifiers) as our belief function. In the case of(More)
Inexpensive structured light sensors can capture rich information from indoor scenes, and scene labeling problems provide a compelling opportunity to make use of this information. In this paper we present a novel conditional random field (CRF) model to effectively utilize depth information for semantic labeling of indoor scenes. At the core of the model, we(More)