Face Recognition

Face Recognition Across Pose and Illumination (Face Image Modeling and Representation) Part 2

Experimental Results Databases We used two databases in our face recognition across pose experiments, the CMU Pose, Illumination, and Expression (PIE) database [46] and the FERET database [39]. Each of these databases contains substantial pose variation. In the pose subset of the CMU PIE database (Fig. 8.4), the 68 subjects are imaged simultaneously under 13 […]

Face Recognition Across Pose and Illumination (Face Image Modeling and Representation) Part 3

Face Recognition Across Pose and Illumination Because appearance-based methods use image intensities directly, they are inherently sensitive to variations in illumination. Drastic changes in illumination such as between indoor and outdoor scenes therefore cause significant problems for appearance-based face recognition algorithms [24, 38]. In this section, we describe two ways to handle illumination variations in […]

Skin Color in Face Analysis (Face Image Modeling and Representation) (Face Recognition) Part 1

Introduction Color is a common feature used in machine vision applications. As a cue, it offers several advantages: easy to understand and use. Implementations can be made computationally fast and efficient, thus providing a low level cue. Under stable and uniform illumination, color cue remains robust against geometrical changes. Its ability to separate the targets […]

Skin Color in Face Analysis (Face Image Modeling and Representation) (Face Recognition) Part 2

Non-canonical Images and Colors If images are not taken under the illumination used in camera calibration, the colors are distorted even more. The distortion will appear as a shift in colors, as can be seen in Fig. 9.7 which displays images taken under four different light sources while the camera is calibrated to one of […]

Skin Color in Face Analysis (Face Image Modeling and Representation) (Face Recognition) Part 3

Color Cue for Face Detection As mentioned above, color is a useful cue for face detection as it can greatly reduce the search area by selecting only the skin-like regions. However, it is obvious that the use of skin color only is not enough to distinguish between faces and other objects with a skin-like appearance […]

Face Aging Modeling (Face Image Modeling and Representation) (Face Recognition) Part 1

Introduction Face recognition accuracy is typically limited by the large intra-class variations caused by factors such as pose, lighting, expression, and age [16]. Therefore, most of the current work on face recognition is focused on compensating for the variations that degrade face recognition performance. However, facial aging has not received adequate attention compared to other […]

Face Aging Modeling (Face Image Modeling and Representation) (Face Recognition) Part 2

Aging Simulation Given a face image of a subject at a certain age, aging simulation involves the construction of the face image of that subject adjusted to a different age. Given a 2D image at age x, the 3D shape,and the textureare first produced by following the preprocessing step described in Sect. 10.2, and then […]

Face Aging Modeling (Face Image Modeling and Representation) (Face Recognition) Part 3

Face Recognition Tests The performance of the aging model is evaluated by comparing the face recognition accuracy of a state-of-the-art matcher before and after aging simulation. The probe set,is    constructed    by selecting one imagefor each subject i at age xi in each database,The   gallery    set    G    = is similarly constructed. Table 10.2 Databases used in […]

Face Detection (Face Recognition Techniques) Part 1

Introduction Face detection is the first step in automated face recognition. Its reliability has a major influence on the performance and usability of the entire face recognition system. Given a single image or a video, an ideal face detector should be able to identify and locate all the present faces regardless of their position, scale, […]

Face Detection (Face Recognition Techniques) Part 2

Learning Weak Classifiers As mentioned earlier, the AdaBoost learning procedure is aimed at learning a sequence of weak classifiers hm(x) and the combining weights am in (11.1). It solves the following three fundamental problems: (1) learning effective features from a large feature set; (2) constructing weak classifiers, each of which is based on one of […]

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