Anthropometric 3D Face Recognition

We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel t...

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Vydáno v:International journal of computer vision Ročník 90; číslo 3; s. 331 - 349
Hlavní autoři: Gupta, Shalini, Markey, Mia K., Bovik, Alan C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Boston Springer US 01.12.2010
Springer
Springer Nature B.V
Témata:
Algorithms
Applied sciences
Artificial Intelligence
Biometry
Computer Imaging
Computer Science
Computer science; control theory; systems
Discrimination in insurance
Errors
Exact sciences and technology
Face
Face recognition
Facial
Human
Image Processing and Computer Vision
Pattern Recognition
Pattern Recognition and Graphics
Pattern recognition. Digital image processing. Computational geometry
Recognition
Studies
Three dimensional
Vision
3D face recognition
Local facial features
Geodesic distances
Anthropometric
Euclidean geometry
Image processing
Face recognition
Anthropometry
Pattern recognition
Classification
Facies
Database
Geodesic
Standard deviation
Tridimensional image
Localization
Automatic recognition
ISSN:0920-5691, 1573-1405
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Popis
Shrnutí:We present a novel anthropometric three dimensional (Anthroface 3D) face recognition algorithm, which is based on a systematically selected set of discriminatory structural characteristics of the human face derived from the existing scientific literature on facial anthropometry. We propose a novel technique for automatically detecting 10 anthropometric facial fiducial points that are associated with these discriminatory anthropometric features. We isolate and employ unique textural and/or structural characteristics of these fiducial points, along with the established anthropometric facial proportions of the human face for detecting them. Lastly, we develop a completely automatic face recognition algorithm that employs facial 3D Euclidean and geodesic distances between these 10 automatically located anthropometric facial fiducial points and a linear discriminant classifier. On a database of 1149 facial images of 118 subjects, we show that the standard deviation of the Euclidean distance of each automatically detected fiducial point from its manually identified position is less than 2.54 mm. We further show that the proposed Anthroface 3D recognition algorithm performs well (equal error rate of 1.98% and a rank 1 recognition rate of 96.8%), out performs three of the existing benchmark 3D face recognition algorithms, and is robust to the observed fiducial point localization errors.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-010-0360-8