Integration of local and global geometrical cues for 3D face recognition

Al-Osaimi, Faisal R.; Bennamoun, Mohammed; Mian, Ajmal

doi:10.1016/j.patcog.2007.07.009

Cited by 42 publications

(25 citation statements)

References 21 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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“…We tested the robustness of single contours under varying conditions that are common in practice, such as small errors in nose tip localization and pose normalization, and different levels of noise. This was done by evaluating the MAP of each contour within the range r= [1,140] mm. Figure 3 shows the following results for each of the contour curves:…”

Section: Single Curve Matchingmentioning

confidence: 99%

“…Many methods focus on recognizing 3D faces with neutral expressions, which is still an active field of research. Recently, Al-Osaimi et al [1] developed a method that combines local and global geometric information of the face in a 2D histogram, extracts a single feature vector, and performs 3D face matching. More challenging is to recognize faces * e-mail: fhaar@cs.uu.nl † e-mail: Remco.Veltkamp@cs.uu.nl under different expressions.…”

Section: Introductionmentioning

confidence: 99%

A 3D face matching framework

Haar

Veltkamp

2008

2008 IEEE International Conference on Shape Modeling and Applications

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Many 3D face matching techniques have been developed to perform face recognition. Among these techniques are variants of 3D facial curve matching, which are techniques that reduce the amount of face data to one or a few 3D curves. The face's central profile, for instance, proved to work well. However, the selection of the optimal set of 3D curves and the best way to match them is still underexposed. We propose a 3D face matching framework that allows profile and contour based face matching. Using this framework we evaluate profile and contour types including those described in literature, and select subsets of facial curves for effective and efficient face matching. Results on the 3D face retrieval track of SHREC'07 (the 3D SHape Retrieval Contest) shows the highest mean average precision achieved so far, using only three facial curves of 45 samples each.

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“…Then the similarity between them was computed to determine whether they belonged to the same person. Al-osaimi et al, [9] integrated local and global geometrical cues in a single compact representation for 3D face recognition. In another method, the human face is considered as a 3D surface, and the global difference between two surfaces provides the distinguishability between faces.…”

Section: Introductionmentioning

confidence: 99%