Human computer interface using hand gesture recognition based on neural network: Fid-Bau Portal

Human computer interface using hand gesture recognition based on neural network

Jalab, Hamid A. / Omer, Herman K.

Gesture is one of the most vivid and dramatic way of communications between human and computer. Hence, there has been a growing interest to create easy-to-use interfaces by directly utilizing the natural communication and management skills of humans. This paper presents a hand gesture interface for controlling media player using neural network. The proposed algorithm recognizes a set of four specific hand gestures, namely: Play, Stop, Forward, and Reverse. Our algorithm is based on four phases, Image acquisition, Hand segmentation, Features extraction, and Classification. A frame from the webcam camera is captured, and then skin detection is used to segment skin regions from background pixels. A new image is created containing hand boundary. Hand shape features extraction, are used to describe the hand gesture. An artificial neural network has been utilized as a gesture classifier, as well. 120 gesture images have been used for training. The obtained average classification rate is 95%. The proposed algorithm develops an alternative input device to control the media player, and also offers different gesture commands and can be useful in real-time applications. Comparisons with other hand gesture recognition systems have revealed that our system shows better performance in terms accuracy.