Gesture-recognition-using-Deep-convoluted-network

Gesture recognition provides real-time data to a computer to make it fulfill the user’s commands. Motion sensors in a device can track and interpret gestures, using them as the primary source of data input. A majority of gesture recognition solutions feature a combination of 3D depth-sensing cameras and infrared cameras together with machine learning systems. Machine learning algorithms are trained based on labeled depth images of hands, allowing them to recognize hand and finger positions.

Gesture recognition consists of three basic levels:

• Detection. With the help of a camera, a device detects hand or body movements, and a machine learning algorithm segments the image to find hand edges and positions.
• Tracking. A device monitors movements frame by frame to capture every movement and provide accurate input for data analysis.
• Recognition. The system tries to find patterns based on the gathered data. When the system finds a match and interprets a gesture, it performs the action associated with this gesture. Feature extraction and classification in the scheme below implements the recognition functionality.