Controlling Media Player with Hand Gestures using Convolutional Neural Network

In today's world, everyone opts for fast interaction with complex systems that ensure a quick response. Thus, with increasing improvement in technology, response time and ease of operations are the concerns. Here is where human-computer interaction comes into play. This interaction is unrestricted and challenges the used devices such as the keyboard and mouse for input. Gesture recognition has been gaining much attention. Gestures are instinctive and are frequently used in day-to-day interactions. Therefore, communicating using gestures with computers creates a whole new standard of interaction. In this project, with the help of computer vision and deep learning techniques, user hand movements (gestures) are used in real-time to control the media player. In this project, seven gestures are defined to control the media players using hand gestures. The proposed web application enables the user to use their local device camera to identify their gesture and execute the control over the media player and similar applications (without any additional hardware). It increases efficiency and makes interaction effortless by letting the user control his/her laptop/desktop from a distance.