Rank-Based Decision Fusion for 3D Shape-Based Face Recognition

Gökberk, Berk; Salah, Albert Ali; Akarun, Lale

doi:10.1007/11527923_106

Cited by 49 publications

(14 citation statements)

References 11 publications

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“…Some other techniques [227,233,234] first project the 3D face data onto a 2D intensity image, whereupon the projected 2D images are processed as standard intensity images. Yet other methods have been proposed for 3D face recognition based on local features [235], local and global geometric cues [236], profiles [237][238][239][240], and the rank-based decision fusion of various shape-based classifiers [241]. Several approaches have also been proposed that integrate 2D texture and 3D shape information.…”

Section: D Model-basedmentioning

confidence: 99%

A Survey of Face Recognition Techniques

Jafri¹,

Arabnia²

2009

Journal of Information Processing Systems

557

269

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Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

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Section: D Model-basedmentioning

confidence: 99%

A Survey of Face Recognition Techniques

Jafri¹,

Arabnia²

2009

Journal of Information Processing Systems

557

269

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show abstract

“…The EGI in turn, describes the shape of an object by distribution of surface normal over the object structure. A 2005 article (Gökberk et al, 2005) compared five approaches to 3D face recognition. They compared methods based on EGI, ICP matching, Range Profile, PCA, and Linear Discriminate Analysis (LDA).…”

Section: Challengesmentioning

confidence: 99%

“…Figure 6 illustrates this approach, which is similar to the one used in (Gökberk et al, 2005) to detect a symmetry plane. As shown in Figure 6, it is assumed that n =(n x , n y , n z ) is the unit normal vector of the detected symmetry plane.…”

Section: Fig 5 Symmetry Profile Analysismentioning

confidence: 99%

Processing and Recognising Faces in 3D Images

Elyan¹,

Doolan²

2011

New Approaches to Characterization and Recognition of Faces

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“…Gokberk et al [12] compare five approaches to 3D face recognition. They compare methods based on EGI, ICP matching, Range Profile, PCA, and Linear Discriminate Analysis LDA.…”

Section: Previous Workmentioning

confidence: 99%