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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Moloti N. created project Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

Medium 18657e03 c017 4a3b b485 57589b45e7a5

Intelligent Home Security: Africa Motion Content encoder decoder using Deep Neural Networks

We propose the use of Drones to help communities enhance their security initiatives, to identify criminals during the day and at night. We use multiple sensors and computer vision algorithms to be able to recognize/detect motion and content in real-time, then automatically send messages to community members cell phones about the criminal activities. Hence, community members may be able to stop house breakings before they even occur.

Machine Intelligence Algorithm Design Methodology

AMCnet: https://github.com/AfricaMachineIntelligence/AMCnet https://devmesh.intel.com/projects/africa-motion-content-network-amcnet

We propose a deep neural network for the prediction of future frames in natural video sequences using CPU. To effectively handle complex evolution of pixels in videos, we propose to decompose the motion and content, two key components generating dynamics in videos. The model is built upon the Encoder-Decoder Convolutional Neural Network and Convolutional LSTM for pixel-level prediction, which independently capture the spatial layout of an image and the corresponding temporal dynamics. By independently modeling motion and content, predicting the next frame reduces to converting the extracted content features into the next frame content by the identified motion features, which simplifies the task of prediction. The model we aim to build should be end-to-end trainable over multiple time steps, and naturally learns to decompose motion and content without separate training. We evaluate the proposed network architecture on human AVA and UCF-101 datasets. We show state-of-the art performance in comparison to recent approaches. This is an end-to-end trainable network architecture running on the CPU with motion and content separation to model the spatio-temporal dynamics for pixel-level future prediction in natural videos.

// We then use this AMCnet pretrained model on the Video feed from the DJI Spark drone, integrated with the Movidius NCS to accelerate real-time object detection neural networks.

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amber p. updated status

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amber pande

Hi Everybody, I am Amber Pande, pursuing undergraduate in Computer Science in India. Java, C, Python, SQL, C++, HTML5 are the programming languages that I have worked with until now.

However, my fields of interest beside core programming are Artificial Intelligence and Gaming. Currently, I am working on a Hackathon project.about Deep Learning The topic of my project is to develop a Web based software for inspection of various buildings from Life and Fire Safety Point of view.

Moreover, for my further study, I am planning to go with Artificial Intelligence and Research for my Master's Degree. Also, I am planning to make a career move as a Deep Learning (AI) research from a core developer.

I am willing to learn a lot from this platform and also do the optimum use of the resources and share my knowledge with the AI communities in India.

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