A Power-Efficient Security Device Leveraging Deep Learning (DL)-Inspired Facial Recognition

Author(s):  
R. S. Saundharya Thejaswini ◽  
S. Rajaraajeswari ◽  
Pethuru Raj
2019 ◽  
Vol 27 ◽  
pp. 04002
Author(s):  
Diego Herrera ◽  
Hiroki Imamura

In the new technological era, facial recognition has become a central issue for a great number of engineers. Currently, there are a great number of techniques for facial recognition, but in this research, we focus on the use of deep learning. The problems with current facial recognition convection systems are that they are developed in non-mobile devices. This research intends to develop a Facial Recognition System implemented in an unmanned aerial vehicle of the quadcopter type. While it is true, there are quadcopters capable of detecting faces and/or shapes and following them, but most are for fun and entertainment. This research focuses on the facial recognition of people with criminal records, for which a neural network is trained. The Caffe framework is used for the training of a convolutional neural network. The system is developed on the NVIDIA Jetson TX2 motherboard. The design and construction of the quadcopter are done from scratch because we need the UAV for adapt to our requirements. This research aims to reduce violence and crime in Latin America.


Machines ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 49
Author(s):  
Anna Boschi ◽  
Francesco Salvetti ◽  
Vittorio Mazzia ◽  
Marcello Chiaberge

The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.


2020 ◽  
Vol 10 (23) ◽  
pp. 12883-12892
Author(s):  
Melanie Clapham ◽  
Ed Miller ◽  
Mary Nguyen ◽  
Chris T. Darimont

Author(s):  
Jimit Gandhi ◽  
Aditya Jeswani ◽  
Fenil Doshi ◽  
Parth Doshi ◽  
Ramchandra S. Mangrulkar

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