title: Robust radial face detection for omnidirectional vision.
abstract: Bio-inspired and non-conventional vision systems are highly researched topics. Among them, omnidirectional vision systems have demonstrated their ability to significantly improve the geometrical interpretation of scenes. However, few researchers have investigated how to perform object detection with such systems. The existing approaches require a geometrical transformation prior to the interpretation of the picture. In this paper, we investigate what must be taken into account and how to process omnidirectional images provided by the sensor. We focus our research on face detection and highlight the fact that particular attention should be paid to the descriptors in order to successfully perform face detection on omnidirectional images. We demonstrate that this choice is critical to obtaining high detection rates. Our results imply that the adaptation of existing object-detection frameworks, designed for perspective images, should be focused on the choice of appropriate image descriptors in the design of the object-detection pipeline.
authors.affiliation: IRSEEM, ESIGELEC, EA 4353, Rouen 76801, France. yohan.dupuis@esigelec.fr
authors.forename: Yohan
authors.initials: Y
authors.lastname: Dupuis
authors.forename: Xavier
authors.initials: X
authors.lastname: Savatier
authors.forename: Jean-Yves
authors.initials: JY
authors.lastname: Ertaud
authors.forename: Pascal
authors.initials: P
authors.lastname: Vasseur
mesh_terms: D000465:Algorithms; D056667:Biometric Identification / Q000295:instrumentation / Q000379:methods*; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*