Mobile robot control using face recognition algorithms

In this paper is presented an algorithm to detect human faces using the orthogonal projection principle PCA (Principal Component Analysis) and a neural network classifier trained offline with a set of training images acquired from a webcam as well as from the CBLC and Manchester databases. The algorithm for the detection and tracking of faces will be implemented in a Visual C + + application, which will be tested on the mobile robot. The purpose of this paper is to implement an image processing algorithm in robotics thus making a human-robot interaction. The experimental results have been achieved on the PeopleBot, a two-wheeled differently driven mobile robot with a castor wheel for stability.

[1]  Takeo Kanade,et al.  Name-It: association of face and name in video , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[4]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Thomas Serre,et al.  Hierarchical classification and feature reduction for fast face detection with support vector machines , 2003, Pattern Recognit..

[7]  Harry Shum,et al.  Kullback-Leibler boosting , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[8]  Andrew Blake,et al.  Computationally efficient face detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[9]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[10]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[11]  Stan Z. Li,et al.  FloatBoost learning and statistical face detection , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Tyng-Luh Liu,et al.  Robust face detection with multi-class boosting , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  J. Edward Jackson,et al.  A User's Guide to Principal Components. , 1991 .

[15]  Bo Wu,et al.  Boosting nested cascade detector for multi-view face detection , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..