Emergent Graphs with PCA-features for Improved Face Recognition

Heinrichs, A.; Müller, Martín; Tewes, Andreas; Würtz, Rolf P.

doi:10.1063/1.2361226

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…However, finding landmarks in the face was not the primary goal of this study. There are several ways to improve automatic processing of data including optimizing our current methods 11 or using additional heuristic methods to find the localization of the face in a given picture (Kalina, in preparation). Compared with the previous study, accuracy dropped slightly from 80 to 75%.…”

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confidence: 99%

Syndrome identification based on 2D analysis software

et al. 2006

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Clinical evaluation of children with developmental delay continues to present a challenge to the clinicians. In many cases, the face provides important information to diagnose a condition. However, database support with respect to facial traits is limited at present. Computer-based analyses of 2D and 3D representations of faces have been developed, but it is unclear how well a larger number of conditions can be handled by such systems. We have therefore analysed 2D pictures of patients each being affected with one of 10 syndromes (fragile X syndrome; Cornelia de Lange syndrome; Williams -Beuren syndrome; Prader -Willi syndrome; Mucopolysaccharidosis type III; Cri-du-chat syndrome; Smith-Lemli -Opitz syndrome; Sotos syndrome; Microdeletion 22q11.2; Noonan syndrome). We can show that a classification accuracy of 475% can be achieved for a computer-based diagnosis among the 10 syndromes, which is about the same accuracy achieved for five syndromes in a previous study. Pairwise discrimination of syndromes ranges from 80 to 99%. Furthermore, we can demonstrate that the criteria used by the computer decisions match clinical observations in many cases. These findings indicate that computerbased picture analysis might be a helpful addition to existing database systems, which are meant to assist in syndrome diagnosis, especially as data acquisition is straightforward and involves off-the-shelf digital camera equipment.

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confidence: 99%

Syndrome identification based on 2D analysis software

et al. 2006

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“…They must be initialized by manual annotation of facial images. In [5] we have presented a method that can automatically build good bunch graphs from only a few manually annotated images. This is also used for invariance learning in the second part of this paper.…”

Section: Elastic Bunch Graph Matchingmentioning

confidence: 99%

Two Kinds of Statistics for Better Face Recognition

Günther

Müller

Würtz

2010

AIP Conference Proceedings

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We briefly review the base techniques of elastic graph matching [1] and elastic bunch graph matching [2], which provide a method for face detection, matching, comparison, and identity decision. We then present a method that combines the advantages of Gabor-labeled graphs with maximum likelihood decision making. The improvements over pure bunch graph matching have been studied, and the method has been successfully applied to large face databases like the one for the Face Recognition Grand Challenge [3]. Finally, we describe a system based on rank order statistics that can learn invariances in a minimally supervised way from a set of examples of individual faces in several situations like different head pose or illumination. Recognition rates are improved significantly without explicit modeling of the image formation process [4].

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“…A comparison of the position of the eye nodes of the found graphs with ground truth data showed that all faces were located with adequate accuracy. A simpler version of this is described in [12]. n ≤ N N ).…”

Section: Landmark Findingmentioning

confidence: 99%

Similarity Rank Correlation for Face Recognition Under Unenrolled Pose

Müller

Heinrichs

Tewes

et al. 2007

Advances in Biometrics

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Abstract. Face recognition systems have to deal with the problem that not all variations of all persons can be enrolled. Rather, the variations of most persons must be modeled. Explicit modeling of different poses is awkward and time consuming. Here, we present a subsystem that builds a model of pose variation by keeping a model database of persons in both poses, additionally to the gallery of clients known in only one pose. An identification or verification decision for probe images is made on the basis of the rank order of similarities with the model database. Identification achieves up to 100% recognition rate on 300 pairs of testing images with 45 degrees pose variation within the CAS-PEAL database, the equal error rate for verification reaches 0.5%.

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Emergent Graphs with PCA-features for Improved Face Recognition

Cited by 5 publications

References 10 publications

Syndrome identification based on 2D analysis software

Syndrome identification based on 2D analysis software

Two Kinds of Statistics for Better Face Recognition

Similarity Rank Correlation for Face Recognition Under Unenrolled Pose

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