• Gowtham S
• Sivadharani S
• Dhanushchandar N A
• Elizabeth R

Many people utilize gesture recognition to engage with technology and others, as well as to express their feelings. Given that hand gesture identification is a high-dimensional pattern recognition challenge, researchers are highly interested in it. The performance of machine learning models is directly impacted by the enormous dimensionality of the problem. Feature extraction and selection can be used to solve the dimensionality issue. In order to do this, a model evaluation that makes use of both human and automatic feature extraction was suggested. While a CNN and BiLSTM were used for the automatic feature extraction, the statistical functions of central tendency were used for the manual feature extraction. Additionally, SVM, ANN, and Softmax classifiers assessed these properties.

Convolutional Neural Network, Feature Extraction, Hand Gesture Recognition, Leap Motion Controller.

"EXPLORING MACHINE LEARNING FOR REAL-TIME GESTURE ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.p396-p406, April-2024, Available :http://www.jetir.org/papers/JETIR2404G48.pdf

"EXPLORING MACHINE LEARNING FOR REAL-TIME GESTURE ANALYSIS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppp396-p406, April-2024, Available at : http://www.jetir.org/papers/JETIR2404G48.pdf