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BHOS Tezisler 2022 17x24sm

THE 3
rd
 INTERNATIONAL SCIENTIFIC CONFERENCES OF STUDENTS AND YOUNG RESEARCHERS 
dedicated to the 99
th
anniversary of the National Leader of Azerbaijan Heydar Aliyev
129
TOUCHLESS HUMAN-MACHINE INTERFACE BASED ON 
INFRARED PROXIMITY SENSING 
Ali Asgarov 
Baku Higher Oil School
Baku, Azerbaijan 
ali.asgarov.std@bhos.edu.az 
Supervisor: Ph.D Associate Professor Ali Parsayan 
Keywords:
Hand gesture recognition, Haar-Like features, Deep Learning, Z-axis filtering, 
3-D Images, Depth Sensor, Infrared (IR) Images, Convolutional Neural Network (CNN). 
Introduction 
Many researchers have attempted to make machines interpret other 
people's intents and information through noncontact methods including 
voice, facial expressions, body motion, and gestures. Gesture is the most 
essential component of human language, and its evolution has an influence 
on the nature and flexibility of human-robot interaction. We can see touch-
less HMI as a viable technology in today’s world, with more promise in areas 
where sanity or outdoor operation are important considerations [1].
In this 
paper, a suitable design exploring some techniques involved in hand and 
finger movement detection, using depth-sensing infrared cameras embedded 
on Xbox Kinect Module is presented. Firstly, 3-D images are generated and 
filtered along the z-axis, then two distinct techniques; Haar-Like Features, 
and Deep Learning using a Convolution Neural Network (CNN), are 
performed on the images to detect hands movement. 
 
Materials and Method 
There are three main steps in the detection system: the input unit, 
processing unit and the output unit. The input unit consists of the Kinetic 
Sensor which generates a RGB image, Infrared image and Depth 
Information. Processing Unit consists of detection of hands in infrared 
images, detection of fingers in the hands, detection of the finger movements. 
Output unit consists of displaying result of hand and finger movement 
detection.

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