A Standalone Application to Scan Barcode using Webcam

When you visit any shopping mall or stores, you must have seen a barcode detector machine which read barcode of the product you purchased and calculate the cost. Finally, you get a receipt of shopping having items you have purchased and cost of respective items. Use of barcode scanner in shopping malls reduces paperwork and digitize shopping by a great extent. However, it still requires human effort. Another problem with this is, it comes at a high cost. changes and durability is also an issue with this barcode scanner machine. Therefore, it became necessary to develop such a system which can automate the process of scanning the products and comes at low cost with low maintenance requirement.

Here, a barcode scanner using python has been developed which can read barcode of the product using webcam, decode the cost of item which is encrypted in barcode and calculate the price of the product, finally at the end of shopping you will get number of items you have purchased, date of purchasing, name of items purchased and the corresponding cost. Developed system is found to be cost effective, durable, easy to use, fully automated the billing process i.e don’t require human effort to scan barcode.

For detail explanation and code please visit here
Demo video of project is available at Youtube.

Development of 3D face Recognition System using Matlab

Several machine learning algorithms for image processing and computer vision applications have been proposed over the past decade. LBP, HAAR are some of the popular algorithms which are widely used for face recognition and yield excellent results. But, most of these algorithms are not suitable for real time recognition in unconstrained environment. Recently, state-of-the-art deep learning techniques have become new favorite over traditional machine learning algorithm. Face recognition application works with images which are nothing but combination of pixel values in range (0–255). Algorithm finds a discriminating pattern in those gray values and consider it as a feature which is considered to be unique for each image. However, in 3D images, no pixel information is present, but only position (x, y, z) of each point is available. This makes it difficult to find pattern in 3D images.

Recently, I started working on FRGC2.0 3D face dataset and initially, I was not able to find enough resources to process 3D images. Finally, after some time, i came across a paper based on 3D face recognition , by Ajmal Mian whom I would like to thank for helpful suggestions and references. I also would like to mention my colleague Jayeeta Chakraborty who equally contributed to develop this project. Source code is availabe at my GitHub repository.

For more deatil and explanantion please visit link : https://towardsdatascience.com/development-of-3d-face-recognition-using-matlab-a54ccc0b7cdd