Search Results for author: M. G. Sarwar Murshed

Found 7 papers, 4 papers with code

Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction

no code implementations26 Nov 2023 M. G. Sarwar Murshed, Syed Konain Abbas, Sandip Purnapatra, Daqing Hou, Faraz Hussain

Most modern fingerprint authentication systems rely on contact-based fingerprints, which require the use of fingerprint scanners or fingerprint sensors for capturing fingerprints during the authentication process.

Segmentation

Deep Age-Invariant Fingerprint Segmentation System

no code implementations6 Mar 2023 M. G. Sarwar Murshed, Keivan Bahmani, Stephanie Schuckers, Faraz Hussain

However, segmenting or auto-localizing all fingerprints in a slap image is a challenging task due to the different orientations of fingerprints, noisy backgrounds, and the smaller size of fingertip components.

Segmentation

Deep Slap Fingerprint Segmentation for Juveniles and Adults

1 code implementation IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) 2021 M. G. Sarwar Murshed, Robert Kline, Keivan Bahmani, Faraz Hussain, Stephanie Schuckers

Subsequently, the dataset is used to evaluate the matching performance of the NFSEG, a slap fingerprint segmentation system developed by NIST, on slaps from adults and juvenile subjects.

Segmentation

FlexServe: Deployment of PyTorch Models as Flexible REST Endpoints

no code implementations29 Feb 2020 Edward Verenich, Alvaro Velasquez, M. G. Sarwar Murshed, Faraz Hussain

The integration of artificial intelligence capabilities into modern software systems is increasingly being simplified through the use of cloud-based machine learning services and representational state transfer architecture design.

The Utility of Feature Reuse: Transfer Learning in Data-Starved Regimes

1 code implementation29 Feb 2020 Rashik Shadman, M. G. Sarwar Murshed, Edward Verenich, Alvaro Velasquez, Faraz Hussain

The use of transfer learning with deep neural networks has increasingly become widespread for deploying well-tested computer vision systems to newer domains, especially those with limited datasets.

Transfer Learning

Machine Learning at the Network Edge: A Survey

1 code implementation31 Jul 2019 M. G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, Faraz Hussain

To address this issue, efforts have been made to place additional computing devices at the edge of the network, i. e close to the IoT devices where the data is generated.

BIG-bench Machine Learning Edge-computing

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