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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Sahil S. created project MapTrace

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MapTrace

THE ISSUE :

There are so many cases of now days where we come to know that there some people are selling or consuming drugs nearby , but some time police remember's that place and some times not and which is not very good thing , and even it will not make easy to give training to new police officers which is not good .

OUR MAGIC SOLUTION :

We have created a app which can analyze all these thing , this app take longitude and latitude value from current location and also ask for name of Drug which is being selling or consuming by suspect and mergers all these datas to heat map and save it to firebase and generates Heat Maps from that .. which is very help full for us..

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SHIVAM K. updated status

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SHIVAM KUMAR ROY

Hello,I am still working on Android development program and web development also. I just want to lean about amchine learnig and AI ,to explore its use in whatever the things are around us.

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Soubhik D. created project Presentify-let's know where we stand

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Presentify-let's know where we stand

Suppose we're speaking in a hall before a hige audience but are unable to know whether or not the audience is interested so that we can mix up with lightning talks, motivational stories, quotes, etc. At that time, our talk starts becoming boring and we start getting the tag of 'unwanted speaker'. This will never happen if we have the data, specifically data of dynamic and running time users' rating of the speakers. There just needs to be a few Realsense Cameras placed optimally in the hall and a person/bot with AR and or VR headset tracks the user's rating collectively (heatmap). Realsense cameras and the headset are connected. This promises to make every talk in the world interesting after gathering enough data.

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Sahil S. updated status

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Sahil Singh

Iam currently working on a life saving IoT device which helps diabetic type-1 patients to automatically regulate their insulin levels. And also gives notification on high or low blood sugar..

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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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Hyunho Choi

I received a B.S. from Department of Information and Statistics, Yonsei University, Wonju, Korea, in 2014, and the M.S. degree in medical biotechnology from Dongguk University, Gyeonggi-do, in 2016. He is currently working toward a Ph.D. degree in Electronic and Computer Engineering at Hanyang University, Seoul, Korea. His research interests include image processing such as noise reduction of the image and image fusion

South Korea

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perry obara

I'm always ready to learn.

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Victor Kinoti

python programmer, telecommunications eng.

Nairobi, Kenya

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Shuo Liu

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

Kelowna, BC, Canada

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