Human-robot interaction based on Haar-like features and eigenfaces

José Barreto, Paulo Menezes, Jorge Dias

Research output: Contribution to journalConference articlepeer-review

52 Scopus citations


This paper describes a machine learning approach for visual object detection and recognition which is capable of processing images rapidly and achieving high detection and recognition rates. This framework is demonstrated on, and in part motivated by, the task of human-robot interaction. There are three main parts on this framework. The first is the person's face detection used as a preprocessing system to the second stage which is the recognition of the face of the person interacting with the robot, and the third one is the hand detection. The detection technique is based on Haar-like features introduced by Viola et al. and then improved by Lienhart et al. The eigenimages and PCA are used in the recognition stage of the system. Used in real-time human-robot interaction applications the system is able to detect and recognise faces at 10.9 frames per second in a PIV 2.2GHz equipped with a USB camera.

Original languageBritish English
Pages (from-to)1888-1893
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Issue number2
StatePublished - 2004
EventProceedings- 2004 IEEE International Conference on Robotics and Automation - New Orleans, LA, United States
Duration: 26 Apr 20041 May 2004


Dive into the research topics of 'Human-robot interaction based on Haar-like features and eigenfaces'. Together they form a unique fingerprint.

Cite this