Geodesic illumination basis: compensating for illumination variations in any pose for face recognition

Ishiyama, Rui; Sakamoto, Shizuo

doi:10.1109/icpr.2002.1047455

Cited by 25 publications

(22 citation statements)

References 11 publications

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“…Other approaches utilize a 3D range image and albedo map of the person's face to render novel images under arbitrary illumination [28], while others are based on a combination of the above [29,30]. The shortcomings of this approach are a) the requirement in practice of large example sets to achieve good reconstructions, b) the increase in the dimensionality of the classification problem by the introduction of the illumination variability, c) the requirement of pixel wise alignment between probe and gallery images which necessitates pose compensation and d) the reliance to simplified reflectance models (e.g.…”

Section: Illumination Compensationmentioning

confidence: 99%

Robust face recognition using 2D and 3D data: Pose and illumination compensation

Malassiotis

Strintzis

2005

Pattern Recognition

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Section: Illumination Compensationmentioning

confidence: 99%

Robust face recognition using 2D and 3D data: Pose and illumination compensation

Malassiotis

Strintzis

2005

Pattern Recognition

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“…The majority of these techniques exploits the low dimensionality of the face space under varying illumination conditions and the Lambertian assumption [40]. They either use several images of the same person recorded under varying illumination conditions [41] or rely on the availability of 3-D face models and albedo maps [42]- [44] to generate novel views. The main shortcoming of this approach is the requirement in practice of large example sets to achieve good reconstructions.…”

Section: Simulating Illuminationmentioning

confidence: 99%

Face localization and authentication using color and depth images

Tsalakanidou

Malassiotis

Strintzis

2005

IEEE Trans. on Image Process.

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Abstract-This paper presents a complete face authentication system integrating both two-dimensional (color or intensity) and three-dimensional (3-D) range data, based on a low-cost 3-D sensor, capable of real-time acquisition of 3-D and color images. Novel algorithms are proposed that exploit depth information to achieve robust face detection and localization under conditions of background clutter, occlusion, face pose alteration, and harsh illumination. The well-known embedded hidden Markov model technique for face authentication is applied to depth maps and color images. To cope with pose and illumination variations, the enrichment of face databases with synthetically generated views is proposed. The performance of the proposed authentication scheme is tested thoroughly on two distinct face databases of significant size. Experimental results demonstrate significant gains resulting from the combined use of depth and color or intensity information.

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“…In the case of the face, the illumination cone can be approximated by a lower-dimensional linear space [12].…”

Section: Geodesic Illumination Basismentioning

confidence: 99%

Face recognition under variable pose and illumination conditions using 3D facial appearance models

Ishiyama

Hamanaka

Sakamoto

2007

Systems & Computers in Japan

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SUMMARYThis paper proposes a method in which an appearance model is constructed from 3D facial shape data that can describe the image variation due to the arbitrariness of the pose and the variation of the illumination, after which recognition is performed by fitting the model to facial images when the illumination conditions and the precise pose are unknown. The appearance model in the proposed method is constructed as follows. The illumination basis for an image of an arbitrary pose is derived from the geodesic illumination basis describing the brightness on the 3D object surface depending on the illumination variations, and an image with the same illumination condition is reproduced as the target image for the unknown illumination conditions. By optimizing the pose to minimize the reproduction error, model fitting with high accuracy is realized even if the exact pose is unknown. By experiment, the number of illumination samples needed in the calculation of the geodesic illumination basis is evaluated and it is verified that the proposed appearance model can describe arbitrary illumination variations in images with various poses. The robustness of the proposed pose optimization method against initial pose estimate is also evaluated. The effectiveness of the proposed method is demonstrated by a recognition experiment using 14,000 facial images taken in various situations, including extreme illumination variations such as backlighting, for a wide range of poses from frontal to 45° upward and 60° sidewise.

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Geodesic illumination basis: compensating for illumination variations in any pose for face recognition

Cited by 25 publications

References 11 publications

Robust face recognition using 2D and 3D data: Pose and illumination compensation

Robust face recognition using 2D and 3D data: Pose and illumination compensation

Face localization and authentication using color and depth images

Face recognition under variable pose and illumination conditions using 3D facial appearance models

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