A Bayesian similarity measure for direct image matching

Moghaddam, B.; Nastar, Chahab; Pentland, Alex

doi:10.1109/icpr.1996.546848

Cited by 129 publications

(65 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The identification of a new face consists of determining among the constructed graphs, the one which maximises the graph similarity function. Another proposed approach to face recognition is the Bayesian Intra/extrapersonal Classifier (BIC) [7] which uses the Bayesian decision theory to divide the difference vectors between pairs of face images into two classes: one representing intrapersonal differences (i.e. differences in a pair of images representing the same person) and extrapersonal differences.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition with Local Binary Patterns

Ahonen

Hadid

Pietikäinen

2004

Lecture Notes in Computer Science

1,774

1,162

View full text Add to dashboard Cite

Abstract. In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. The recognition is performed using a nearest neighbour classifier in the computed feature space with Chi square as a dissimilarity measure. Extensive experiments clearly show the superiority of the proposed scheme over all considered methods (PCA, Bayesian Intra/extrapersonal Classifier and Elastic Bunch Graph Matching) on FERET tests which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction.

show abstract

Section: Introductionmentioning

confidence: 99%

Face Recognition with Local Binary Patterns

Ahonen

Hadid

Pietikäinen

2004

Lecture Notes in Computer Science

1,774

1,162

View full text Add to dashboard Cite

show abstract

“…This leads to more complex minimization problems, which may be still efficiently solved using dynamic programming, if the number of possible displacements is small. Note that it is difficult to embed the XYI image warping approach [6] into the model (5) as the implicit XYI cost function depends on the intensity values.…”

Section: Relating Td and Idmmentioning

confidence: 99%

“…usage of hierarchical confidence refinement [4] or models for representing large subsets of the prototypes [5]. An approach motivated by deformable models, which is related to the invariance approaches covered in this paper, was proposed in [6] and tested on a face database. In comparison to TD and IDM it uses different assumptions about the allowed transformations and their cost.…”

Section: Introductionmentioning

confidence: 99%

Experiments with an extended tangent distance

Keysers

Dahmen

Theiner

et al.

Proceedings 15th International Conference on Pattern Recognition. ICPR-2000

View full text Add to dashboard Cite

show abstract

“…Further it requires stronger image normalization. Keeping these pits and falls of this algorithm in view researchers have emphasis a lot on image normalization resulting in improvement of the system [9,10]. A proposed solution to these problems in computer vision applications is to use a point distribution model (PDM).…”

Section: Related Workmentioning

confidence: 99%

Image normalization for face recognition using 3D model

Riaz

Beetz

Radig

2009

2009 International Conference on Information and Communication Technologies

View full text Add to dashboard Cite

Abstract-This paper describes an image segmentation and normalization technique using 3D point distribution model and its counterpart in 2D space. This segmentation is efficient to work for holistic image recognition algorithm. The results have been tested with face recognition application using Cohn Kanade Facial Expressions Database (CKFED). The approach follows by fitting a model to face image and registering it to a standard template. The models consist of distribution of points in 2D and 3D. We extract a set of feature vectors from normalized images using principal components analysis and using them for a binary decision tree for classification. A promising recognition rate of up to 98.75% has been achieved using 3D model and 92.93% using 2D model emphasizing the goodness of our normalization. The experiments have been performed on more than 3500 face images of the database. This algorithm is capable to work in real time in the presence of facial expressions.

show abstract

A Bayesian similarity measure for direct image matching

Cited by 129 publications

References 11 publications

Face Recognition with Local Binary Patterns

Face Recognition with Local Binary Patterns

Experiments with an extended tangent distance

Image normalization for face recognition using 3D model

Contact Info

Product

Resources

About