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Images of faces, represented as high-dimensional pixel arrays, often belong to a manifold of intrinsically low dimension. Face recognition, and computer vision research in general, has witnessed a growing interest in techniques that capitalize on this observation and apply algebraic and statistical tools for extraction and analysis of the underlying manifold. In this chapter, we describe in roughly...
In this chapter, we will present three groups of dimension reduction algorithms for subspace based face recognition. Specifically, we present the general mean criteria and the max-min distance analysis (MMDA) algorithm; manifold learning algorithms, including the discriminative locality alignment (DLA) and manifold elastic net (MEN); and the transfer subspace learning framework. Experiments on face...
Feature extraction is one of the fundamental tasks in computer vision and image processing. Respectively, the task of selecting the best set of features to describe faces for recognition, verification, localization, or detection, is a fundamental problem in face biometrics. In this chapter, we review the most popular and successful features for face biometrics. In general, one should include complete...
In order to interpret images of faces (e.g., for recognition), it is important to have a model of the different ways that a face may appear. Though faces vary widely, changes can be broken down into two categories—changes in shape and changes in the texture (patterns of pixel values) across the face—that are largely due to differences between individuals, but also due to changes in expression, viewpoint...
In this chapter, we present the Morphable Model, a three-dimensional (3D) representation that enables the accurate modeling of any illumination and pose as well as the separation of these variations from the rest (identity and expression). The Morphable Model is a generative model consisting of a linear 3D shape and appearance model plus an imaging model, which maps the 3D surface onto an image. The...
In this chapter, we show that effective systems can account for the effects of lighting using fewer than 10 degrees of freedom. This can have considerable impact on the speed and accuracy of recognition systems. We will describe theoretical results that, with some simplifying assumptions, prove the validity of low-dimensional, linear approximations to the set of images produced by a face.
The last decade has seen automatic face recognition evolve from small-scale research systems to a wide range of commercial products. Driven by the FERET face database and evaluation protocol, the currently best commercial systems achieve verification accuracies comparable to those of fingerprint recognizers. In these experiments, only frontal face images taken under controlled lighting conditions...
This chapter deals with the role of color in facial image analysis such as face detection and recognition. First, we introduce the use of color information in computer vision in general and in the field of facial image analysis in particular. Then, we give an introduction to color formation and discuss the effect of illumination on color appearance, and its consequences. The skin data can come from...
One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that affects both the 3D shape of the face and its texture (e.g., wrinkles). These shape and texture changes degrade the performance of automatic face...
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