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Abstract:
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 images.
The estimate is achieved by fitting a
statistical, morphable model of 3D faces to images.
The model is
learned from a set of textured 3D scans of heads.
We describe the construction of the morphable model,
an algorithm to fit the model to images, and a
framework for face identification.
In this framework, faces are represented by
model parameters for 3D shape and texture.
We present results obtained with 4488 images
from the publicly available CMU-PIE database,
and 1940 images from the FERET database.
