Abstract

We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system differs from the preceding one [1] in three respects. Phase information is used for accurate node positioning. Object-adapted graphs are used to handle large rotations in depth. Image graph extraction is based on a novel data structure, the bunch graph, which is constructed from a small set of sample image graphs. Index Terms: face recognition, different poses, Gabor wavelets, elastic graph matching, bunch graph, ARPA/ARL FERET database, Bochum database. 1 Introduction The system presented here is based on a face recognition system described in [1]. In this preceding system, individual faces were represented by a rectangular graph, each node labele...

Keywords

Artificial intelligencePattern recognition (psychology)Computer visionFacial recognition systemComputer scienceFace (sociological concept)GraphMatching (statistics)Image (mathematics)MathematicsTheoretical computer scienceStatistics

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Publication Info

Year
2022
Type
book
Pages
355-396
Citations
1770
Access
Closed

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Laurenz Wiskott, Jean‐Marc Fellous, Norbert Krüger et al. (2022). Face Recognition by Elastic Bunch Graph Matching*†. INTELLIGENT BIOMETRIC TECHNIQUES in FINGERPRINT and FACE RECOGNITION , 355-396. https://doi.org/10.1201/9780203750520-11

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DOI
10.1201/9780203750520-11

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Data completeness: 77%