A novel indexing approach for multimedia image databases

Vissac, Mathieu; Dugelay, Jean‐Luc; Rose, Ken

doi:10.1109/mmsp.1999.793804

Cited by 4 publications

(2 citation statements)

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“…Note that, while the Elastic Graph Matching (EGM) [20] also defines a distance between face images, it does not make use of a probabilistic framework. However, other approaches related to EGM, such as [21], have made an attempt to define a probabilistic distance.…”

Section: Introductionmentioning

confidence: 99%

A probabilistic model of face mapping with local transformations and its application to person recognition

Perronnin

Dugelay

Rose

2005

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-This paper proposes a new measure of "distance" between faces. This measure involves the estimation of the set of possible transformations between face images of the same person. The global transformation, which is assumed to be too complex for direct modeling, is approximated by a patchwork of local transformations, under a constraint imposing consistency between neighboring local transformations. The proposed system of local transformations and neighboring constraints is embedded within the probabilistic framework of a two-dimensional hidden Markov model. More specifically, we model two types of intraclass variabilities involving variations in facial expressions and illumination, respectively. The performance of the resulting method is assessed on a large data set consisting of four face databases. In particular, it is shown to outperform a leading approach to face recognition, namely, the Bayesian intra/extrapersonal classifier.

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Section: Introductionmentioning

confidence: 99%

A probabilistic model of face mapping with local transformations and its application to person recognition

Perronnin

Dugelay

Rose

2005

IEEE Trans. Pattern Anal. Machine Intell.

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“…Local transformations and neighboring constraints are embedded within a probabilistic framework using twodimensional hidden Markov models (2D HMMs). A similar approach for general content-based image retrieval appeared first in [4] and preliminary results were presented on a database of binary images.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Model for Face Transformation with Application to Person Identification

Perronnin

Dugelay

Rose

2004

EURASIP J. Adv. Signal Process.

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A novel approach for content-based image retrieval and its specialization to face recognition are described. While most face recognition techniques aim at modeling faces, our goal is to model the transformation between face images of the same person. As a global face transformation may be too complex to be modeled directly, it is approximated by a collection of local transformations with a constraint that imposes consistency between neighboring transformations. Local transformations and neighborhood constraints are embedded within a probabilistic framework using two-dimensional hidden Markov models (2D HMMs). We further introduce a new efficient technique, called turbo-HMM (T-HMM) for approximating intractable 2D HMMs. Experimental results on a face identification task show that our novel approach compares favorably to the popular eigenfaces and fisherfaces algorithms.

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