1,068 research outputs found
Constrained Design of Deep Iris Networks
Despite the promise of recent deep neural networks in the iris recognition
setting, there are vital properties of the classic IrisCode which are almost
unable to be achieved with current deep iris networks: the compactness of model
and the small number of computing operations (FLOPs). This paper re-models the
iris network design process as a constrained optimization problem which takes
model size and computation into account as learning criteria. On one hand, this
allows us to fully automate the network design process to search for the best
iris network confined to the computation and model compactness constraints. On
the other hand, it allows us to investigate the optimality of the classic
IrisCode and recent iris networks. It also allows us to learn an optimal iris
network and demonstrate state-of-the-art performance with less computation and
memory requirements
A Convolution Neural Network Engine for Sclera Recognition
The world is shifting to the digital era in an enormous pace. This rise in the digital technology has created plenty of applications in the digital space, which demands a secured environment for transacting and authenticating the genuineness of end users. Biometric systems and its applications has seen great potentials in its usability in the tech industries. Among various biometric traits, sclera trait is attracting researchers from experimenting and exploring its characteristics for recognition systems. This paper, which is first of its kind, explores the power of Convolution Neural Network (CNN) for sclera recognition by developing a neural model that trains its neural engine for a recognition system. To do so, the proposed work uses the standard benchmark dataset called Sclera Segmentation and Recognition Benchmarking Competition (SSRBC 2015) dataset, which comprises of 734 images which are captured at different viewing angles from 30 different classes. The proposed methodology results showcases the potential of neural learning towards sclera recognition system
Colab NAS: Obtaining lightweight task-specific convolutional neural networks following Occam's razor
The current trend of applying transfer learning from convolutional neural
networks (CNNs) trained on large datasets can be an overkill when the target
application is a custom and delimited problem, with enough data to train a
network from scratch. On the other hand, the training of custom and lighter
CNNs requires expertise, in the from-scratch case, and or high-end resources,
as in the case of hardware-aware neural architecture search (HW NAS), limiting
access to the technology by non-habitual NN developers.
For this reason, we present ColabNAS, an affordable HW NAS technique for
producing lightweight task-specific CNNs. Its novel derivative-free search
strategy, inspired by Occam's razor, allows to obtain state-of-the-art results
on the Visual Wake Word dataset, a standard TinyML benchmark, in just 3.1 GPU
hours using free online GPU services such as Google Colaboratory and Kaggle
Kernel
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