Abstract—Human face detection and tracking is an important research area having wide application in human machine interface, content-based image retrieval, video coding, gesture recognition, crowd surveillance and face recognition. Human face detection is extremely important and simultaneously a difficult problem in computer vision, mainly due to the dynamics and high degree of variability of the head. A large number of effective algorithms have been proposed for face detection in grey scale images ranging from simple edge based methods to composite high-level approaches using modern and advanced pattern recognition approaches. The aim of the paper is to compare Gradient vector flow and silhouettes, two of the most widely used algorithms in the area of face detection. Both the algorithms were applied on a common database and the results were compared. This is the first paper which evaluates the runtime analysis of Gradient vector field methodology and compares with silhouettes segmentation technique. The paper also explains the factors affecting the performance and error incurred by both the algorithms. Finally, results are explained which proves the superiority of the silhouette segmentation method over Gradient vector flow method.
Index Terms—Face detection, gradient vector flow (GVF), active contour flow, silhoutte.
Amarjot Singh, K.V. Karthik are with National Institute of Technology, Warangal, 506004, India (e-mail: amarjotsingh@ieee.org).
Shivesh Bajpai is Indian School of Mines Dhanbad, Jharkhand, India.
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Cite: Shivesh Bajpai, Amarjot Singh, and K.V. Karthik, "An Experimental Comparison of Face Detection Algorithms,"
International Journal of Computer Theory and Engineering vol. 5, no. 1, pp. 47-51, 2013.
