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Recognition of people in images is important for many applications in computer vision. This paper presents an experimental study on pedestrian classification. We investigate the recently developed kernelbased features in order to represent an image and two learning algorithms: the popular Support Vector Machine (SVM) and Genetic Programming (GP). Numerical experiments are performed on a benchmark dataset consisting of pedestrian and non-pedestrian (labeled) images captured in outdoor urban environments and indicate that the evolutionary classifier is able to perform better over SVM.

2010 Mathematics Subject Classification. 68T05,91E45.1998 CR Categories and Descriptors. code I.2.6 [Learning]: - Concept learning.

Key words and phrases. Object recognition, Kernel descriptors, Support Vector Machines, Kernel selection.This paper has been presented at the International Conference KEPT2013: Knowledge Engineering Principles and Techniques, organized by Babeșs-Bolyai University, Cluj-Napoca, July 5-7 2013.