ABSTRACT

Automatic human gesture recognition from camera images is an interesting topic for developing intelligent vision systems. In this paper, we propose a convolution neural network (CNN) method to recognize hand gestures of human task activities from a camera image. To achieve the robustness performance, the skin model and the calibration of hand position and orientation are applied to obtain the training and testing data for the CNN. Since the light condition seriously affects the skin colour, we adopt a Gaussian Mixture model (GMM) to train the skin model which is used to robustly filter out non-skin colours of an image. The calibration of hand position and orientation aims at translating and rotating the hand image to a neutral pose. Then the calibrated images are used to train the CNN. In our experiment, we provided a validation of the proposed method on recognizing human gestures which shows robust results with various hand positions and orientations and light conditions. Our experimental evaluation of seven subjects performing seven hand gestures with average recognition accuracies around 95.96% shows the feasibility and reliability of the proposed method.

EXISTING SYSTEM

This work is a CNN-based human hand gesture recognition system. CNN is a research branch of neural networks. Using a CNN to learn human gestures, there is no need to develop complicated algorithms to extract image features and learn them. Through the convolution and sub-sampling layers of a CNN, invariant features are allowed with little dislocation. To reduce the effect of various hand poses of a hand gesture type on the recognition accuracies, the principal axis of the hand is found to calibrate the image in this work. Calibrated images are advantageous to a CNN to learn and recognize correctly.

PROPOSED SYSTEM

From the camera image input, the hand is extracted by skin colour segmentation. The skin model is trained by a Gaussian Mixture model to classify skin colour and non-skin colour. After that, the calibration of hand position and orientation is used to translate and rotate the hand image to a neutral pose. The calibrated image is fed to the CNN to train or test the network. For continuous hand motion, the post-processing is used to filter out the noises of the results from the CNN. Each block of the flowchart is described as follows.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10