Nasal Patches and Curves for Expression-Robust 3D Face Recognition

The potential of the nasal region for expression robust 3D face recognition is thoroughly investigated by a novel five-step algorithm. First, the nose tip location is coarsely detected and the face is segmented, aligned and the nasal region cropped. Then, a very accurate and consistent nasal landmar...

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Veröffentlicht in:IEEE transactions on pattern analysis and machine intelligence Jg. 39; H. 5; S. 995 - 1007
Hauptverfasser: Emambakhsh, Mehryar, Evans, Adrian
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.05.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithm design and analysis
Algorithms
Face
Face recognition
facial landmarking
Facial recognition technology
Feature extraction
feature selection
Gabor wavelets
Genetic algorithms
Morlet wavelet
Noise reduction
Nose
nose region
Patches (structures)
Robustness
State of the art
surface normals
Three-dimensional displays
ISSN:0162-8828, 2160-9292, 1939-3539
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Zusammenfassung:The potential of the nasal region for expression robust 3D face recognition is thoroughly investigated by a novel five-step algorithm. First, the nose tip location is coarsely detected and the face is segmented, aligned and the nasal region cropped. Then, a very accurate and consistent nasal landmarking algorithm detects seven keypoints on the nasal region. In the third step, a feature extraction algorithm based on the surface normals of Gabor-wavelet filtered depth maps is utilised and, then, a set of spherical patches and curves are localised over the nasal region to provide the feature descriptors. The last step applies a genetic algorithm-based feature selector to detect the most stable patches and curves over different facial expressions. The algorithm provides the highest reported nasal region-based recognition ranks on the FRGC, Bosphorus and BU-3DFE datasets. The results are comparable with, and in many cases better than, many state-of-the-art 3D face recognition algorithms, which use the whole facial domain. The proposed method does not rely on sophisticated alignment or denoising steps, is very robust when only one sample per subject is used in the gallery, and does not require a training step for the landmarking algorithm.
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ISSN:0162-8828
2160-9292
1939-3539
DOI:10.1109/TPAMI.2016.2565473