Abstract
Abstract: This work presents a computer-vision-based application for recognizing hand gestures. A live video feed is captured by a camera, and a still image is extracted from that feed with the aid of an interface. At least once per count hand gesture (one, two, three, four, and five), the system is trained. After that, the system is given a test gesture to see if it can identify it. Several algorithms that are capable of distinguishing a hand gesture were studied. It was determined that the highest rate of accuracy was achieved by using the computational neural network known as the Alexnet algorithm. Traditionally, systems have used data gloves or markers as a means of input. We are free to use the system however we like. In this way, the user can make natural hand gestures in front of the camera. The system implemented serves as an extendable basis for future work toward a fully robust hand gesture recognition system, which is still the subject of intensive research and development.
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