Finding Faces in Cluttered Still Images with Few Examples

Wieghardt, Jan; Loos, Hartmut S.

doi:10.1007/3-540-44668-0_142

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…The test image was automatically labeled using elastic graph matching, 12,14 which has been proven to be among the best methods for face recognition 12,13 and face finding. 15 For classification of the faces, the bunch graph is matched to an image by optimizing the similarity between the automatically labeled image graph and bunch graph, which is defined by the node similarity averaged over all 48 nodes. The node similarity is the maximum of all jet similarities at the node.…”

Section: Methodsmentioning

confidence: 99%

Computer-based recognition of dysmorphic faces

et al. 2003

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Genetic syndromes often involve craniofacial malformations. We have investigated whether a computer can recognize disease-specific facial patterns in unrelated individuals. For this, 55 photographs (256 Â 256 pixel) of patients with mucopolysaccharidosis type III (n ¼ 6), Cornelia de Lange (n ¼ 12), fragile X (n ¼ 12), Prader -Willi (n ¼ 12), and Williams-Beuren (n ¼ 13) syndromes were preprocessed by a Gabor wavelet transformation. By comparing the feature vectors at 32 facial nodes, 42/55 (76%) of the patients were correctly classified. In another four patients (7%), the correct and an incorrect diagnosis scored equally well. Clinical geneticists who were shown the same photographs achieved a recognition rate of 62%. Our results prove that certain syndromes are associated with a specific facial pattern and that this pattern can be described in mathematical terms.

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Section: Methodsmentioning

confidence: 99%

Computer-based recognition of dysmorphic faces

et al. 2003

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“…First the resolution has been reduced to 256×256 pixels. A Facefinder [7] has been applied, that uses bunch graph matching with three bunch graphs of different sizes. The area of the best fit has been scaled to an image of size 128×128.…”

Section: Feret Databasementioning

confidence: 99%

Emergent Graphs with PCA-features for Improved Face Recognition

Heinrichs¹,

Müller²,

Tewes³

et al. 2006

AIP Conference Proceedings

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Built on the principles of "Learning from Nature" and "Self-organization" Elastic Bunch Graph Matching for face recognition is a defining example for Organic Computing methodology. Here, we follow these principles further to advance the method in two respects. First, the requirement for manual annotation of landmarks is reduced to one single face, from which a self-organizing selection process gradually builds up the bunches by adding the most similar face to the bunch graph and then recalculating the matching. Second, the resulting bunches are replaced by the principal components of the nodes of all persons in the database. The similarity function is restricted to a suitable subset of these components. The additional self-organizing processes lead to improved precision of landmark localization and recognition rates. Altogether, an improved data structure for face storage has emerged from the simple presentation of examples in a minimally supervised way.

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1-Click Learning of Object Models for Recognition

Loos

Malsburg

2002

Biologically Motivated Computer Vision

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Finding Faces in Cluttered Still Images with Few Examples

Cited by 3 publications

References 8 publications

Computer-based recognition of dysmorphic faces

Computer-based recognition of dysmorphic faces

Emergent Graphs with PCA-features for Improved Face Recognition

1-Click Learning of Object Models for Recognition

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