Intelligent Vision System for Human-Robot Interface
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 33093
Intelligent Vision System for Human-Robot Interface

Authors: Al-Amin Bhuiyan, Chang Hong Liu

Abstract:

This paper addresses the development of an intelligent vision system for human-robot interaction. The two novel contributions of this paper are 1) Detection of human faces and 2) Localizing the eye. The method is based on visual attributes of human skin colors and geometrical analysis of face skeleton. This paper introduces a spatial domain filtering method named ?Fuzzily skewed filter' which incorporates Fuzzy rules for deciding the gray level of pixels in the image in their neighborhoods and takes advantages of both the median and averaging filters. The effectiveness of the method has been justified over implementing the eye tracking commands to an entertainment robot, named ''AIBO''.

Keywords: Fuzzily skewed filter, human-robot interface, rmscontrast, skin color segmentation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071348

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1431

References:


[1] H. Shinn, and H. Hui-Ling, "Facial modeling from an uncalibrated face image using a coarse-to-fine genetic algorithm", Pattern Recognition, Vol. 34, No. 8, 2001, pp. 1015-1031.
[2] F. Goudail, E. Lange, T. Iwamoto, K. Kazuo, and N. Otsu, "Face recognition system using loacal autocorrelations and multiscale integration", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 10, 1996, pp. 1024-1028.
[3] H. Rowley, B. Shumeet, and T. Kanade, "Neural network-based face detection", IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, 1998, pp. 23-37.
[4] M. Turk, and A. Pentland, "Eigenfaces for recognition", Journal of cognitive neuroscience, Vol. 3, No. 1, 1991, pp. 71-86.
[5] D. Tampe, "Heuristic filtering and reliable calibration methods for video based pupil-tracking systems, Instruments and Computers, 25(2), 1990, 137-142.
[6] K. Sung, and T. Poggio, "Example-based learning for view-based human face detection", IEEE Transactions on Pattern Analysis and Machine Intelligence ,Vol. 20, No. 1, 1998, pp. 39-50.
[7] K. Lam, and Y. Hong, "Locating and extracting the eye in human face images", Pattern Recognition, Vol. 29, No. 5, 1996, pp. 771-779.
[8] B. Moghaddam, and A. Pentland, "Face recognition using View-Based Modular Eigenspaces", Proc. of Automatic Systems for the Identification and Inspection of Humans, SPIE Vol. 2277, 1994.
[9] E. Peli, "Contrast in Complex Images", Journal of Optical Society, Vol. 7, No. 10, 1990, pp. 2032-2040.
[10] Y. Tae-Woong, O. Il-Seok, "A fast algorithm for tracking human faces based on chromatic histograms", Pattern Recognition Letters, Vol. 20, No. 10, 1999, pp. 967-978.
[11] R.C. Gonzalez, R.E. Woods, Digital image processing, Prentice Hall, Inc., 2nd Edition, London, 2002, pp. 88-93.
[12] J. Jantzen, "Tutorial on Fuzzy Logic", www.iau.dtu.dk/~jj/pubs/logic.pdf
[13] D. Marius, S. Pennathur, and Klint Rose, "Face detection using color thresholding and eigenimage template matching", ww.stanford.edu/class/ee368/Project_03/Project/reports/
[14] M.A. Bhuiyan, V. Ampornaramveth, S. Muto, and H. Ueno, "On Tracking of Eye for Human-Robot Interface", International Journal of Robotics and Automation, Vol. 19, No. 1, 2004, pp. 42-54.