Handbook of Face Recognition
Containing a comprehensive account of face recognition research and technology, this handbook provides coverage of the concepts, methods, and algorithms for automated face detection and recognition.
TABLE OF CONTENTS Handbook of Face Recognition
Chapter 13: Face Databases
Ralph Gross
Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA. rgross@cs.cmu.edu
Because of its nonrigidity and complex three-dimensional (3D) structure, the appearance of a face is affected by a large number of factors including identity, face pose, illumination, facial expression, age, occlusion, and facial hair. The development of algorithms robust to these variations requires databases of sufficient size that include carefully controlled variations of these factors. Furthermore, common databases are necessary to comparatively evaluate algorithms. Collecting a high quality database is a resource-intensive task: but the availability of public face databases is important for the advancement of the field. In this chapter we review 27 publicly available databases for face recognition, face detection, and facial expression analysis.
1 Databases for Face Recognition
Face recognition continues to be one of the most popular research areas of computer vision and machine learning. Along with the development of face recognition algorithms, a comparatively large number of face databases have been collected. However, many of these databases are tailored to the specific needs of the algorithm under development. In this section we review publicly available databases that are of demonstrated use to others in the community. At the beginning of each subsection a table summarizing the key features of the database is provided, including (where available) the number of subjects, recording conditions, image resolution, and total number of images. Table 13.1 gives an overview of the recording conditions for all databases discussed in this section. Owing to space constraints not all databases...
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