Biometric Hand Recognition Using Neural Networks

The article discusses the issue of using artificial neural networks for recognizing the conditionally graphical designations of electrical engineering, in particular, the convolutional neural networks and the R-CNN object recognition model, which is most suitable for solving the task at hand. Recognition of images of a specific picture is a task set for the complex information processing systems, as well as control and decision-making systems. The classification of various technological or natural objects, analog and digital signals is developed by a set of specific characteristics and properties. Defining the type and features of an object finds its application in different branches of science: machine learning, diagnostics, meteorology, video surveillance and security systems, in virtual reality systems and image search. However, research has not yet been carried out for solving the applied problems and achieving the required parameters (e.g. in recognizing conditional graphical symbols of electrical engineering). The neural networks have been found to have the highest quality and most promising among all mathematical models and methods of pattern recognition. As for the interactivity, the output result of image recognition work is a necessary and sufficient answer, which does not have a stable work on the variability of objects within categories and their invariant transformations. The scheme of the model R-CNN has been studied in detail, the importance of the training sample and its influence on the quality of pattern recognition by the neural network have been grounded. The application of the RoI Pooling method for object recognition in the image is shown in general, due to which there have been selected several regions of interest indicated through the bounding boxes.

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Hand recognition by wavelet transforms and neural networks

10.1117/12.213067 ◽

1995 ◽

Cited By ~ 2

Author(s):

Wei Wang ◽

Zhonghao Bao ◽

Qiang Meng ◽

Gerald M. Flachs ◽

Jay B. Jordan ◽

...

Keyword(s):

Neural Networks ◽

Wavelet Transforms ◽

Hand Recognition

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CURSOR MANIPULATION WITH HAND RECOGNITION USING COMPUTER VISION

INFORMATION TECHNOLOGY IN INDUSTRY ◽

10.17762/itii.v9i1.298 ◽

2021 ◽

Vol 9 (1) ◽

pp. 1455-1456

Author(s):

Mandar Salvi, Shravan Kegade, Aniket Shinde, Bhanu Tekwani

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Convolutional Neural Networks ◽

Hand Movement ◽

Computing System ◽

Video Frame ◽

Software Program ◽

Hand Recognition

This paper aims to make a software program which will Track/Monitor your hand movement in front of the screen through a webcam and will move the cursor of the computing system with respect to your hand movement and can do certain fixed tasks like Right Click, Left Click, Scroll, Drag, Switch Between Programs, Go back, Forward, etc. This program will work in background and use convolutional Neural Networks Model (SSD) to convolve each and every video frame coming from input and at the end will classify the image into classes after further processing of the predicted class it will do necessary operations on Mouse/ Trackpad driver to perform desired operations.

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