Curvature-based face surface recognition using spherical correlation. Principal directions for curved object recognition

Tanaka, Hiromi T.; Ikeda, M.; Chiaki, H.

doi:10.1109/afgr.1998.670977

Cited by 164 publications

(61 citation statements)

References 6 publications

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“…Moreover, Chang et al (1997) used the weighted sums of three normal components of 3D images to produce synthetic 2D images with different illumination effects. Tanaka, Ikeda, and Chiaki (1998) also represented the face using EGI, and used a spherical correlation of EGI for recognition. Beumier and Acheroy (2001) constructed a structured lighted 3D face acquisition system for face authentication.…”

Section: Introductionmentioning

confidence: 99%

Face recognition using 2D and disparity eigenface

Sun

Chen

et al. 2007

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Face recognition using 2D and disparity eigenface

Sun

Chen

et al. 2007

Expert Systems with Applications

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“…At each point on the surface, the magnitude and direction of the minimum and maximum normal curvatures are calculated. In the 3D domain, many researchers have handled the 3D face recognition problem using differential geometry tools for computing curvatures [151,152]. However, the computation of curvature is neither accurate nor reliable.…”

Section: Fusion Of Imaging Modalities For Face Recognitionmentioning

confidence: 99%

Recent advances in visual and infrared face recognition—a review

Kong

Jae-young

Abidi

et al. 2005

Computer Vision and Image Understanding

497

227

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Face recognition is a rapidly growing research area due to increasing demands for security in commercial and law enforcement applications. This paper provides an up-to-date review of research efforts in face recognition techniques based on two-dimensional (2D) images in the visual and infrared (IR) spectra. Face recognition systems based on visual images have reached a significant level of maturity with some practical success. However, the performance of visual face recognition may degrade under poor illumination conditions or for subjects of various skin colors. IR imagery represents a viable alternative to visible imaging in the search for a robust and practical identification system. While visual face recognition systems perform relatively reliably under controlled illumination conditions, thermal IR face recognition systems are advantageous when there is no control over illumination or for detecting disguised faces. Face recognition using 3D images is another active area of face recognition, which provides robust face recognition with changes in pose. Recent research has also demonstrated that the fusion of different imaging modalities and spectral components can improve the overall performance of face recognition.

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“…The early applications of 3D face recognition include Cartoux, LaPreste, and Richetin (1989), Lee and Milios (1990), Gordon (1991), Nagamine, Uemura, and Masuda (1992), Achermann, B, Jiang, and Bunke (1997), , and Tanaka, Ikeda, and Chiaki (1998). Recently, techniques have been developed and applied to 3D face recognition.…”

Section: Introductionmentioning

confidence: 98%