Need a better way to find faces in a crowd? This paper explores the use of Genetic Algorithms (GAs) for efficient face detection and verification in images, specifically targeting the location of a particular individual. The two steps in solving this problem are: first, face regions must be extracted from the image(s) (face detection) and second, candidate faces must be compared against a face of interest (face verification). It addresses the challenge of searching a vast image space without prior knowledge of face location or size. The GAs locate image sub-windows containing faces. Each sub-window is evaluated using a fitness function containing two terms: the first term favors sub-windows containing faces while the second term favors sub-windows containing faces similar to the face of interest. Both terms have been derived using the theory of eigenspaces. The performance of the proposed genetic-search approach is demonstrated through a series of increasingly complex scenes, highlighting its potential for applications like surveillance and security systems.
Appropriate for International Journal on Artificial Intelligence Tools, this paper presents the utilization of genetic algorithms for face detection and verification, which are key techniques in computer vision. It highlights intelligent systems and their application in image analysis.