Three-dimensional face recognition using combinations of surface feature map subspace components

Heseltine, Thomas; Pears, Nick; Austin, Jim

doi:10.1016/j.imavis.2006.12.008

Cited by 48 publications

(25 citation statements)

References 12 publications

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“…These are the extension of the FRGC dataset, the ND-2006 database [78,79], the CASIA 3D Face dataset [80,81], the Gavadb database [82], the York 3D dataset [83,84] and the Texas 3D face recognition database [85,86]. The ND-2006 contains 888 subjects with multiple images per subject displaying posed happiness, disgust, sadness and surprise.…”

Section: Methodsmentioning

confidence: 99%

Static and dynamic 3D facial expression recognition: A comprehensive survey

Sandbach

Zafeiriou

Pantić

et al. 2012

Image and Vision Computing

324

164

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a b s t r a c t a r t i c l e i n f oAutomatic facial expression recognition constitutes an active research field due to the latest advances in computing technology that make the user's experience a clear priority. The majority of work conducted in this area involves 2D imagery, despite the problems this presents due to inherent pose and illumination variations. In order to deal with these problems, 3D and 4D (dynamic 3D) recordings are increasingly used in expression analysis research. In this paper we survey the recent advances in 3D and 4D facial expression recognition. We discuss developments in 3D facial data acquisition and tracking, and present currently available 3D/4D face databases suitable for 3D/4D facial expressions analysis as well as the existing facial expression recognition systems that exploit either 3D or 4D data in detail. Finally, challenges that have to be addressed if 3D facial expression recognition systems are to become a part of future applications are extensively discussed.

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Section: Methodsmentioning

confidence: 99%

Static and dynamic 3D facial expression recognition: A comprehensive survey

Sandbach

Zafeiriou

Pantić

et al. 2012

Image and Vision Computing

324

164

View full text Add to dashboard Cite

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“…The lines of the T matrix are the R matrix's eigenvectors, according to (5), where for B V argument there is a result in a form of a transformation vector B TV according to (6).…”

Section: Multi-channel Recognition System With An Independent Distancmentioning

confidence: 99%

“…(4) In order to define the distance of V B vector from G class, Karhunen-Loeve (K-L) transform with transformation matrix T are used. The lines of the T matrix are the R matrix's eigenvectors, according to (5), where for V B argument there is a result in a form of a transformation vector TV B according to (6). 2 algorithm suitable for signal classification is very useful for emitter identification and radar classification process, as shown in the work [29,30].…”

Section: Multi-channel Recognition System With An Independent Distancmentioning

confidence: 99%

“…In the literature, terms such as source emission patterns separating surfaces in the measurable features space are widely used to describe pattern recognition and pattern classification [3,4,5]. Taking into account the fact above, radar emitter recognition and classification is based on defining the location of the emission source from the above separating surfaces.…”

Section: Introductionmentioning

confidence: 99%

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A method of feature selection in the aspect of specific identification of radar signals

Dudczyk

2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. This article presents an important task of classification, i.e. mapping surfaces which separate patterns in feature space in the scope of radar emitter recognition (RER) and classification. Assigning a tested radar to a particular class is based on defining its location from the discriminating areas. In order to carry out the classification process, it is necessary to define metrics in the feature space as it is essential to estimate the distance of a classified radar from the centre of the class. The method presented in this article is based on extraction and selection of distinctive features, which can be received in the process of specific emitter identification (SEI) of radar signals, and on the minimum distance classification. The author suggests a RER system which consists of a few independent channels. The task of each channel is to calculate the distance of the tested radar from a given class and finally, set the correct identification coefficient for each recognized radar. Thus, a multichannel system with independent distance measurement is obtained, which makes it possible to recognize particular radar copies. This system is implemented in electronic intelligence (ELINT) system and tested in real battlefield conditions. Key words: radar emitter recognition (RER), specific emitter identification (SEI), minimum distance classification, ELINT system. cedure is the problem to define how to estimate the distance of a tested radar emitter signal from the centre of the class taking into consideration variance and correlation of vector's features. The RER method also provides a solution when the features of radar patterns are not linearly separable. RER method bases on the analysis of basic measurable parameters of the radar signal (such as RF, PW, PRI) as result of which it is possible to extract additional distinctive features. The RER process is called specific emitter identification (SEI). Additionally extracted distinctive features, which are received in the process of RER, may be a product of out-of-band radiation of radar devices [12]. These features may be of fractal type, which is presented in the works [13,14,15]. The received features may also be a product of inter-pulse modulation [16] and intrapulse analysis of a radar signal [17]. Of course, there are more complicated approaches, which offer effective methods for solving the classification task (i.e. mapping separating surfaces). These are based on solving the linear approximation task recurrently, using gradient methods and nonlinear approximation [18], nonlinear approximation of random function [19] and other methods for adaptive regression splines, classification and approximation [20,21]. This is a typical solution for identification systems such as perceptrons or artificial neural network (ANN), e.g., support vector machine networks (SVM) [22] using Widrow-Hoff learning algorithms, Adaline ANN or the method based on back-propagating errors and neural network classifier with low discrepancy optimization [23,24]. Also, the Fourier tr...

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“…It has been demonstrated that 3-D facial recognition methods can achieve significantly better accuracy than their 2-D counterparts, rivaling fingerprint recognition (Bronstein et al, 2005;Heseltine et al, 2008;Kakadiaris et al, 2007;Queirolo et al, 2009). By measuring the geometry of rigid features, 3-D facial recognition avoids such pitfalls of 2-D With the technology we developed, high-quality 3-D faces can be captured even when the subject is moving.…”

Section: Biometrics For Homeland Securitymentioning

confidence: 99%

High-resolution, High-speed 3-D Dynamically Deformable Shape Measurement Using Digital Fringe Projection Techniques

Zhang¹

2010

Advances in Measurement Systems

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Three-dimensional face recognition using combinations of surface feature map subspace components

Cited by 48 publications

References 12 publications

Static and dynamic 3D facial expression recognition: A comprehensive survey

Static and dynamic 3D facial expression recognition: A comprehensive survey

A method of feature selection in the aspect of specific identification of radar signals

High-resolution, High-speed 3-D Dynamically Deformable Shape Measurement Using Digital Fringe Projection Techniques

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