3D facial image analysis for human identification

Nakagawa, Takashi; Uemura, Tomomasa; Masuda, I.

doi:10.1109/icpr.1992.201567

Cited by 60 publications

(36 citation statements)

References 5 publications

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“…As it explained in our previous work [3] Nagamine et al [6] tackled face recognition by exploring facial profiles. They used horizontal section (extracted as an intersection of a face surface with a plane parallel to X-Z plane), vertical section (extracted as an intersection of a face surface with a plane parallel to Y-Z plane) and circular cross section (extracted as an intersection of a face surface with a cylinder (axis on Y-Z plane and parallel to Z-axis)).…”

Section: D Facial Recognition Studiesmentioning

confidence: 99%

Parallel Cascade Correlation Neural Network Methods for 3D Facial Recognition: A Preliminary Study

Amman¹,

Jordan²

2015

JCC

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This paper explores the possibility of using multi-core programming model that implements the Cascade correlation neural networks technique (CCNNs), to enhance the classification phase of 3D facial recognition system, after extracting robust and distinguishable features. This research provides a comprehensive summary of the 3D facial recognition systems, as well as the state-of-theart for the Parallel Cascade Correlation Neural Networks methods (PCCNNs). Moreover, it highlights the lack of literature that combined between distributed and shared memory model which leads to novel possibility of taking advantage of the strengths of both approaches in order to construct an efficient parallel computing system for 3D facial recognition.

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Section: D Facial Recognition Studiesmentioning

confidence: 99%

Parallel Cascade Correlation Neural Network Methods for 3D Facial Recognition: A Preliminary Study

Amman¹,

Jordan²

2015

JCC

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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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“…For example (BenAbdelkader & Griffin, 2005) used seven manually selected land-mark points. Similarly in (Nagamine et al, 1992) it is necessary to identify several landmark points for example nose tip and eye corners which can then be used to register the face.…”

Section: Automatic Feature Extractionmentioning

confidence: 99%

“…Thus, each face becomes a point in the feature space and the comparisons were carried out using the nearest neighbouring algorithm. Nagamine (Nagamine et al, 1992) extracted five feature points and used it to standardize face pose, matching various curves or profiles though the face data. According to this experiment the best recognition rates were achieved using vertical profiles that pass through the central region of the face.…”

Section: Challengesmentioning

confidence: 99%