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Figure 1:  Flow chart Of Hand Gesture Recognition System  THE 3



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Figure 1: 
Flow chart Of Hand Gesture Recognition System 


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
130
Static object detection algorithm using Haar-like Features 
Considering a native RGB image, Haar-like features are generated features 
that are a representation of pixel intensities up to a particular location in an 
image, this implies that Haar-like features can be calculated fast and easily 
since they are only mathematical sums based on the concept of integral 
images [1] .
Dynamic object detection using Convolutional Neural Network 
Deep Learning is algorithms can be used in any field where data of the 
past events applies to generate predictive models with high levels of 
accuracy. For implementation in this design, a deep learning model is trained 
to detect hands and fingers in infrared images by feeding a large enough 
datasets of hand images. 
Results and Discussion 
Hands were detected using Haar-Like Features, which was written in 
Python programming language. The results include but not limited to the 
successful detection of hands in different positions. A sample
of a real-time 
image is as shown in Figure 2. 
 
Figure 2: 
Hand Detection Using (Haar Cascades) Haar -Like Features 
Dynamic object detection using a convolutional neural network (CNN) 
The results of utilizing a CNN (Deep Learning Model) were satisfactory 
since the model was trained on a broad range of infrared pictures and the 
dataset was split into test and train samples with a
total of 20,000 photos. 
 
 

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