Alternative search techniques for face detection using location estimation and binary features |
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| Authors: | Venkatesh Bala Subburaman Sébastien Marcel |
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| Affiliation: | 1. Idiap Research Institute, PO Box 592, CH-1920 Martigny, Switzerland;2. École Polytechnique Fédérale de Lausanne (EPFL), Station 14, CH-1015 Lausanne, Switzerland;1. School of Electronics and Information Engineering, Soochow University, Suzhou 215006, China;2. Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, United States;3. Hospital of the University of Pennsylvania, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104-6021, United States;1. University of Manchester, Manchester, United Kingdom;2. ARM Ltd., Cambridge, United Kingdom;3. National University of Sciences and Technology, Islamabad, Pakistan;4. University of the Basque Country, San Sebastian, Spain;5. University of Utah, Salt Lake City, UT, USA;6. University of Ulster, Belfast, United Kingdom;1. The James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA;2. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA;3. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA |
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| Abstract: | The sliding window approach is the most widely used technique to detect an object from an image. In the past few years, classifiers have been improved in many ways to increase the scanning speed. Apart from the classifier design (such as the cascade), the scanning speed also depends on a number of different factors (such as the grid spacing, and the scale at which the image is searched). When the scanning grid spacing is larger than the tolerance of the trained classifier it suffers from low detections. In this paper, we present a technique to reduce the number of missed detections when fewer subwindows are processed in the sliding window approach for face detection. This is achieved by using a small patch to predict the location of the face within a local search area. We use simple binary features and a decision tree for location estimation as it proved to be efficient for our application. We also show that by using a simple interest point detector based on quantized gradient orientation, as the front-end to the proposed location estimation technique, we can further improve the performance. Experimental evaluation on several face databases show better detection rate and speed with our proposed approach when fewer number of subwindows are processed compared to the standard scanning technique. |
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