4,135 research outputs found
Biometrics-as-a-Service: A Framework to Promote Innovative Biometric Recognition in the Cloud
Biometric recognition, or simply biometrics, is the use of biological
attributes such as face, fingerprints or iris in order to recognize an
individual in an automated manner. A key application of biometrics is
authentication; i.e., using said biological attributes to provide access by
verifying the claimed identity of an individual. This paper presents a
framework for Biometrics-as-a-Service (BaaS) that performs biometric matching
operations in the cloud, while relying on simple and ubiquitous consumer
devices such as smartphones. Further, the framework promotes innovation by
providing interfaces for a plurality of software developers to upload their
matching algorithms to the cloud. When a biometric authentication request is
submitted, the system uses a criteria to automatically select an appropriate
matching algorithm. Every time a particular algorithm is selected, the
corresponding developer is rendered a micropayment. This creates an innovative
and competitive ecosystem that benefits both software developers and the
consumers. As a case study, we have implemented the following: (a) an ocular
recognition system using a mobile web interface providing user access to a
biometric authentication service, and (b) a Linux-based virtual machine
environment used by software developers for algorithm development and
submission
Binary Weighted Memristive Analog Deep Neural Network for Near-Sensor Edge Processing
The memristive crossbar aims to implement analog weighted neural network,
however, the realistic implementation of such crossbar arrays is not possible
due to limited switching states of memristive devices. In this work, we propose
the design of an analog deep neural network with binary weight update through
backpropagation algorithm using binary state memristive devices. We show that
such networks can be successfully used for image processing task and has the
advantage of lower power consumption and small on-chip area in comparison with
digital counterparts. The proposed network was benchmarked for MNIST
handwritten digits recognition achieving an accuracy of approximately 90%
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