Face Recognition

Local Representation of Facial Features (Face Image Modeling and Representation) (Face Recognition) Part 3

Constructing Gabor Features Gabor features are constructed by convolution of an input imagewith the filter in (4.9) The convolution produces a response image τξ of the same size. Only a single filter rarely succeeds but the response images are computed for a “bank” of filters tuned on various frequencies and orientations. The frequencies are typically […]

Face Alignment Models (Face Image Modeling and Representation) (Face Recognition) Part 1

Introduction In building models of facial appearance, we adopt a statistical approach that learns the ways in which the shape and texture of the face vary across a range of images. We rely on obtaining a suitably large and representative training set of images of faces, each of which is annotated with a set of […]

Face Alignment Models (Face Image Modeling and Representation) (Face Recognition) Part 2

Statistical Models of Texture Though the shape of a face may give a weak indication of identity, the texture of the face provides a far stronger cue for recognition. We therefore apply similar techniques to those used to build a shape model in order to build a model of texture, given a set of training […]

Face Alignment Models (Face Image Modeling and Representation) (Face Recognition) Part 3

Iterative Model Refinement Local searches on their own, however, are prone to spurious matches due to noisy data and unmodelled image properties. To ensure that the estimated shape agrees with the statistical model learned from training data (see Sect. 5.1.1), we regularise our solution by fitting the shape model to the local matches. Hopefully, this […]

Morphable Models of Faces (Face Image Modeling and Representation) (Face Recognition) Part 1

Introduction Our approach is based on an analysis by synthesis framework. In this framework, an input image is analyzed by searching for the parameters of a generative model such that the generated image is as similar as possible to the input image. The parameters are then used for high-level tasks such as identification. To be […]

Morphable Models of Faces (Face Image Modeling and Representation) (Face Recognition) Part 2

Regularized Morphable Model The correspondence estimation, detailed in Sect. 6.2.2, may, for some scans, be wrong in some regions. In this section, we present a scheme aiming to improve the correspondence by regularizing it using statistics derived from scans that do not present correspondence errors. This is achieved by modifying the model construction: probabilistic PCA […]

Morphable Models of Faces (Face Image Modeling and Representation) (Face Recognition) Part 3

Identification Confidence In this section, we present an automated technique for assessing the quality of the fitting in terms of a fitting score (FS). We show that the fitting score is correlated with identification performance and hence, may be used as an identification confidence measure. This method was first presented by Blanz et al. [9]. […]

Illumination Modeling for Face Recognition (Face Image Modeling and Representation) Part 1

Introduction Changes in lighting can produce large variability in the appearance of faces, as illustrated in Fig. 7.1. Characterizing this variability is fundamental to understanding how to account for the effects of lighting on face recognition. In this topic, we will discuss solutions to a problem: Given (1) a three-dimensional description of a face, its […]

Illumination Modeling for Face Recognition (Face Image Modeling and Representation) Part 2

Properties of the Convolution Kernel The Funk-Hecke theorem implies that when producing the reflectance function, r , the amplitude of the light, I, at every order n is scaled by a factor that depends only on the convolution kernel, k. We can use this to infer analytically what frequencies dominate r. To achieve this, we […]

Face Recognition Across Pose and Illumination (Face Image Modeling and Representation) Part 1

Introduction The most recent evaluation of commercial face recognition systems shows the level of performance for face verification of the best systems to be on par with fingerprint recognizers for frontal, uniformly illuminated faces [38]. Recognizing faces reliably across changes in pose and illumination has proved to be a much more difficult problem [9, 24, […]

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