• Anirban Chakraborty
• Devendren V
• Ishan Kumar

Similar technologies can be used for knowledge exchange for two individuals. These could be words or motions. Movement recognition involves the detection and acknowledgment of movements from all forms of body’s movement, but only from the face and the hand. This is a way of conveying knowledge through movements made by users. This provides important dimensions for behavioural and human-computer interaction. user interface. Many methods are usable, including MATLAB, Artificial Neural Networking, etc. There are several solutions. This paper offers an in-depth examination of how neural networks enable movements to be understood more naturally. This involves three phases: image processing, retrieval and identification of features. In the first step, the picture is taken with an average frame rate with a webcam, a digital camera. Attributes are extracted with the input picture during the second phase. The features can be angle between fingertips, open, no-open, closed or semi-closed fingers, and finger recognition. The artificial neural network structure is primarily used for image recognition.

"Facial Expression and Gesture Identification using Artificial Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.983-987, December 2018, Available :http://www.jetir.org/papers/JETIRDZ06128.pdf

"Facial Expression and Gesture Identification using Artificial Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp983-987, December 2018, Available at : http://www.jetir.org/papers/JETIRDZ06128.pdf