Edison Vein ID System

Edison Vein ID System

Low-Cost Intel-Edison-Powered User Authentication System that uses an Individual’s Unique Finger-vein Pattern (Presented at Intel ISEF '17)

Artificial Intelligence, Internet of Things

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Description

While biometrics have become a vital part of user authentication in technological platforms, they suffer from issues like fabrication, high costs, and vulnerability to physical damage that make them difficult to implement in low-cost scenarios. To meet this need, this project authenticates identities through inexpensive peripheral vein biometrics with the Edison Vein ID (EVID) system.

EVID consists of a low-cost NIR camera and low-power NIR LED array. The array emits ~850 nanometer light, which is absorbed by the finger veins’ deoxygenated blood and detected by the camera. Through the Intel Edison Compute Model, a computer vision algorithm captures a raw image, performs contrast-limited adaptive histogram equalization to increase the veins’ contrast, and reduces image noise through a Bilateral Gaussian Filter that preserves the veins’ edges. Finally, a binary threshold segments the image and extracts the unique vein structure, which is then registered as a biometric template.

A MATLAB normalized cross correlation algorithm computes a matching score [0-1] to compare an input image against a template for authentication. This process was repeated with all registered templates to test EVID’s ability to use input images to identify individuals. A 0.573 match score threshold for the verification and identification processes was determined based on False Acceptance Rates and False Rejection Rates according to Biometric Evaluation Standards.

EVID captured, processed, and stored peripheral vein images from 30+ samples, using these templates to authenticate and identify individuals from over 170 images. Future work includes using multi-modular biometrics to create the most secure identity verification system.

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Intel ISEF 2nd Place in System Software Category

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Daniel T. created project CAVSIM

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CAVSIM

In recent years, research over information exchange between vehicles has been growing, with the goal to improve safety and efficiency in traffic. This project describes the development of a multi-agent system for simulation of connected vehicles in road crossings, in order to remove the need for traffic lights. The system was developed using the C# programming language and Boris.NET platform for communication between agents. The user can specify the desired environments using JSON files which are read at the simulation startup. A 2D graphical interface was also created to view the simulation, so that it can follow an agent or stay at a map position.

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This work presents the development of a prototype for the recognition of universal facial expressions. This technic provides an alternative way of gathering data from the user, being able for usage as an input way for information systems. For faces detection and extraction of their characteristics technics of Computer Vision and Digital Image Processing are employed, implemented by the dlib library with Intel RealSense. The classification into facial expressions is performed by an Artificial Neural Network, of the multilayer perceptron kind.

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Adam M. created project TASS PVL Computer Vision Hub

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TASS PVL Computer Vision Hub

DESCRIPTION:

TASS PVL is a sister project to the original TASS Hub project. As with TASS Hub, TASS PVL is a local server which homes an IoT connected A.I. powered by the Intel® Computer Vision SDK Beta. The hub can connect to multiple IP cameras and two Realsense cameras. First, the program detects if there is a face, or faces, present in the frames, and if so passes the frames through the trained model to determine whether the face is a known person or an intruder. In the event of a known person or intruder, the server communicates with the IoT JumpWay which executes the relevant commands that set by rules, for instance, controlling other devices on the network or raising alarms in applications etc.

INTEL® TECHNOLOGY

TASS PVL uses the following Intel technologies:

  • Intel® Core i7 NUC
  • Intel® Computer Vision SDK Beta
  • Intel® Realsense (R200,F200)

IOT CONNECTIVITY:

The IoT connectivity is managed by the TechBubble IoT JumpWay, the TechBubble Technologies IoT PaaS which primarily, at this point, uses secure MQTT protocol. Rules can be set up that can be triggered by sensor values/warning messages/device status messages and identified known people or intruder alerts. These rules allow connected devices to interact with each other autonomously, providing an automated smart home/business environment.

ARTIFICIAL INTELLIGENCE:

TASS PVL uses the Intel Computer Vision SDK Beta to provide the system with Artificial Intelligence. For other uses of A.I. used in the sister project TASS PVL, follow this link.

INTELLILAN MANAGEMENT:

The IntelliLan Management Console/Applications are essentially IoT JumpWay applications, capable of controlling all IntelliLan devices on their network and communicating with the IoT JumpWay. Users can use the console and manage their devices using their voice which is powered by TIA, an A.I. agent developed to assist home and business owners to use TechBubble web and IoT systems.

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Apocalypse Rider

Tired of feeling sick playing racing VR Games? Try Apocalypse Rider now!

In the scorched wasteland, speed is all that matters!

Apocalypse Rider is a VR arcade motorcycle game where you must prevail the high-speed wasteland roads, avoid the hostile traffic and keep surviving, speeding and RIDING!

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Rishabh S. updated status

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Rishabh Shukla

Hello there!

I'm Information Technology undergrad at Maharaja Agrasen Institute of Technology in India. I am a Backend and Mobile developer specialising in Node, Android, Python and iOS. I've also created some Augmented Reality projects in past with ARKit and Vuforia. My latest research is in the field of Image Processing, in which I created an IOT based Rover which follows a required path without any errors. I used Edge Detection technique in it as well. I am also very interested in deep learning and NLP. I've worked with Tensorflow and Keras. I'm excited to know the what projects you've been working on in those fields!

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Gokula Krishnan Santhanam

Masters student @ ETH Zurich, Deep Learning Researcher working on both fundamentals and applications of Deep Learning technologies.

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Mate Kisantal

Machine Learning oriented Aerospace Control Engineering student, with some experience in self-driving car control design.

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perry obara

I'm always ready to learn.

Nairobi, Kenya

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python programmer, telecommunications eng.

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I am studying electronic engineering at University of British Columbia (Okanagan Campus ), Canada.

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Arslan Ahmed

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Akhilesh Rao

2360 SE Morrison St, Portland, OR 97214, USA

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