Achieving Illumination Invariance Using Image Filters
Ognjen Arandjelović, Roberto Cipoll · 2007
In this chapter we are interested in accurately recognizing human faces in the presence of large and unpredictable illumination changes.Our aim is to do this in a setup realistic for most practical applications, that is, without overly constraining the conditions in which image data is acquired.Specifically, this means that people's motion and head poses are largely uncontrolled, the amount of available training data is limited to a single short sequence per person, and image quality is low.In conditions such as these, invariance to changing lighting is perhaps the most significant practical challenge for face recognition algorithms.The illumination setup in which recognition is performed is in most cases impractical to control, its physics difficult to accurately model and face appearance differences due to changing illumination are often larger than those differences between individuals [1].Additionally, the nature of most realworld applications is such that prompt, often real-time system response is needed, demanding appropriately efficient as well as robust matching algorithms.In this chapter we describe a novel framework for rapid recognition under varying illumination, based on simple image filtering techniques.The framework is very general and we demonstrate that it offers a dramatic performance improvement when used with a wide range of filters and different baseline matching algorithms, without sacrificing their computational efficiency. Previous work and its limitationsThe choice of representation, that is, the model used to describe a person's face is central to the problem of automatic face recognition.Consider the components of a generic face recognition system schematically shown in Figure 1.A number of approaches in the literature use relatively complex facial and scene models that explicitly separate extrinsic and intrinsic variables which affect appearance.In most cases, the complexity of these models makes it impossible to compute model parameters as a closed-form expression ("Model parameter recovery" in Figure 1).Rather, model fitting is performed through an iterative optimization scheme.In the 3D Morphable Model of Blanz and Vetter [7], for example, the shape and texture of a novel face are recovered through gradient descent by minimizing the discrepancy between the observed and predicted appearance.Similarly, in Elastic Bunch Graph Matching [8, 23], gradient descent is used to