Face recognition based on fitting a 3D morphable model
نویسندگان
چکیده
منابع مشابه
Face Recognition Based on Fitting a 3D Morphable Model
This paper presents a method for face recognition across variations in pose, ranging from frontal to profile views, and across a wide range of illuminations, including cast shadows and specular reflections. To account for these variations, the algorithm simulates the process of image formation in 3D space, using computer graphics, and it estimates 3D shape and texture of faces from single image...
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3D Morphable Face Models are a powerful tool in computer vision. They consist of a PCA model of face shape and colour information and allow to reconstruct a 3D face from a single 2D image. 3D Morphable Face Models are used for 3D head pose estimation, face analysis, face recognition, and, more recently, facial landmark detection and tracking. However, they are not as widely used as 2D methods t...
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We demonstrate a real-time face tracking system, which works based on morphable 3D model fitting. The technical details are partly presented in [2]. Given a target user’s individual morphable 3D model in the form of a combination of 3D linear bases, our system is capable of tracking the rigid and non-rigid motion of the target in live video. Furthermore, thanks to the tracking accuracy of our s...
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We address the problem of 3D-assisted 2D face recognition in scenarios when the input image is subject to degradations or exhibits intra-personal variations not captured by the 3D model. The proposed solution involves a novel approach to learn a subspace spanned by perturbations caused by the missing modes of variation and image degradations, using 3D face data reconstructed from 2D images rath...
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As a classic statistical model of 3D facial shape and texture, 3D Morphable Model (3DMM) is widely used in facial analysis, e.g., model fitting, image synthesis. Conventional 3DMM is learned from a set of well-controlled 2D face images with associated 3D face scans, and represented by two sets of PCA basis functions. Due to the type and amount of training data, as well as the linear bases, the ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2003
ISSN: 0162-8828
DOI: 10.1109/tpami.2003.1227983