Authors:
Diego Ballotta
;
Guido Borghi
;
Roberto Vezzani
and
Rita Cucchiara
Affiliation:
University of Modena and Reggio Emilia, Italy
Keyword(s):
Head Detection, Head Localization, Depth Maps, Convolutional Neural Network.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
Abstract:
Head detection and localization is a demanding task and a key element for many computer vision applications,
like video surveillance, Human Computer Interaction and face analysis. The stunning amount of work done
for detecting faces on RGB images, together with the availability of huge face datasets, allowed to setup very
effective systems on that domain. However, due to illumination issues, infrared or depth cameras may be
required in real applications. In this paper, we introduce a novel method for head detection on depth images
that exploits the classification ability of deep learning approaches. In addition to reduce the dependency on
the external illumination, depth images implicitly embed useful information to deal with the scale of the target
objects. Two public datasets have been exploited: the first one, called Pandora, is used to train a deep binary
classifier with face and non-face images. The second one, collected by Cornell University, is used to perform
a cross
-dataset test during daily activities in unconstrained environments. Experimental results show that the
proposed method overcomes the performance of state-of-art methods working on depth images.
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