• Indhumathi R
• Geetha A

This proposed work aims at developing real time approach to interact with computers. The human computer interaction is a fast growing research area at present. The purpose of this hands-free computer interfaces to replace the traditional input devices such as keyboard and mouse with vision-based interfaces. This work is also more useful for the people who are having physical disabilities. The proposed work recognizes head gestures in real time. In this work the head movements are detected by rotation invariant approach and Haar cascaded classifiers. The detected head gestures are recognized as head-left, head-right, head-down and head-origin using SVM and KNN classifiers under machine learning approach. The Hog features are extracted for models. The proposed system has been tested with real time dataset and it performs better than the other existing approaches.

Hands- free Interfaces, Vision-based Interfaces, SVM, KNN, Haar Cascade Classifier, HOG

"MACHINE LEARNING APPROACH FOR HEAD GESTURE CLASSIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.175-183, December-2018, Available :http://www.jetir.org/papers/JETIR1812C24.pdf

"MACHINE LEARNING APPROACH FOR HEAD GESTURE CLASSIFICATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp175-183, December-2018, Available at : http://www.jetir.org/papers/JETIR1812C24.pdf