Fusion of Visual and Thermal Face Recognition Techniques: A ...

differently. Outside elements are brightness, size, lighting, position, and other
surroundings. Robust face recognition requires identifying individuals despite these
variations. Much research effort has been concentrated on face recognition tasks in which
only a single image or at most a few images of each person are available and a major
concern has been scalability to large databases containing thousands of people.
Application areas of face recognition technology include identification for law
enforcement, matching of photographs on credit cards or driver’s licenses, access control
to secure computer networks and facilities such as government buildings and courthouses,
authentication for secure banking and financial transactions, automatic screening at
airports for known terrorists, and video surveillance usage [1].
Face recognition addresses the problem of identifying or verifying one or more
persons of interest in the scene by comparing input faces with the face images stored in a
database. While humans quickly and easily recognize faces under variable situations or
even after several years of separation, the problem of machine face recognition is still a
highly challenging task in pattern recognition and computer vision.
Face recognition in outdoor environments is a challenging machine vision task
especially where illumination varies greatly. Performance of visual face recognition is
sensitive to variations in illumination conditions [2]. Since faces are essentially 3D
objects, lighting changes can cast significant shadows on a face. This is one of the
primary reasons why current face recognition technology is constrained to indoor access
control applications where illumination is well controlled. Light reflected from human
faces also varies significantly from person to person. This variability, coupled with
dynamic lighting conditions, causes a serious problem. The use of an artificial
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