Abstract
Face recognition under varying pose is a challenging problem, especially when illumination variations are also present. In this paper, we propose to address one of the most challenging scenarios in face recognition. That is, to identify a subject from a test image that is acquired under dierent pose and illumination condition from only one training sample (also known as a gallery image) of this subject in the database. For example, the test image could be semifrontal and illuminated by multiple lighting sources while the corresponding training image is frontal under a single lighting source. Under the assumption of Lambertian reflectance, the spherical harmonics representation has proved to be effective in modeling illumination variations for a fixed pose. In this paper, we extend the spherical harmonics representation to encode pose information. More specifically, we utilize the fact that 2D harmonic basis images at different poses are related by close-form linear transformations, and give a more convenient transformation matrix to be directly used for basis images. An immediate application is that we can easily synthesize a different view of a subject under arbitrary lighting conditions by changing the coefficients of the spherical harmonics representation. A more important result is an efficient face recognition method, based on the orthonormality of the linear transformations, for solving the above-mentioned challenging scenario. Thus, we directly project a nonfrontal view test image onto the space of frontal view harmonic basis images. The impact of some empirical factors due to the projection is embedded in a sparse warping matrix; for most cases, we show that the recognition performance does not deteriorate after warping the test image to the frontal view. Very good recognition results are obtained using this method for both synthetic and challenging real images.
Highlights
Face recognition is one of the most successful applications of image analysis and understanding [1]
Built upon the success of earlier efforts, recent research has focused on robust face recognition to handle the issue of significant difference between a test image and its corresponding training images
The answer is yes, and the reason lies on the fact that 2D harmonic basis images at different poses are related by close-form linear transformations
Summary
Face recognition is one of the most successful applications of image analysis and understanding [1]. According to the theory of spherical harmonics representation [4, 5], this implies that we can synthesize from one image under a fixed pose and lighting to an image acquired under different poses and arbitrary lightings We generate the training images at the frontal pose and under various illumination conditions, and the test images at different poses, under arbitrary lighting conditions, all using Vetter’s 3D face database [10]. The pose-encoded spherical harmonic representation is illustrated in Section 3 where we derive a more convenient transformation matrix to analytically synthesize basis images at one pose from those at another pose.
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