research-article

Bob: a free signal processing and machine learning toolbox for researchers

Authors:

Laurent El-Shafey,

Manuel Günther,

Christopher McCool,

Sébastien MarcelAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 1449 - 1452

https://doi.org/10.1145/2393347.2396517

Published: 29 October 2012 Publication History

Abstract

Bob is a free signal processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, Switzerland. The toolbox is designed to meet the needs of researchers by reducing development time and efficiently processing data. Firstly, Bob provides a researcher-friendly Python environment for rapid development. Secondly, efficient processing of large amounts of multimedia data is provided by fast C++ implementations of identified bottlenecks. The Python environment is integrated seamlessly with the C++ library, which ensures the library is easy to use and extensible. Thirdly, Bob supports reproducible research through its integrated experimental protocols for several databases. Finally, a strong emphasis is placed on code clarity, documentation, and thorough unit testing. Bob is thus an attractive resource for researchers due to this unique combination of ease of use, efficiency, extensibility and transparency. Bob is an open-source library and an ongoing community effort.

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Cited By

Otroshi Shahreza HMarcel S(2024)Template Inversion Attack Using Synthetic Face Images Against Real Face Recognition SystemsIEEE Transactions on Biometrics, Behavior, and Identity Science10.1109/TBIOM.2024.33917596:3(374-384)Online publication date: Jul-2024
https://doi.org/10.1109/TBIOM.2024.3391759
George AMarcel S(2024)Modality Agnostic Heterogeneous Face Recognition with Switch Style Modulators2024 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB62174.2024.10744437(1-10)Online publication date: 15-Sep-2024
https://doi.org/10.1109/IJCB62174.2024.10744437
George AMarcel S(2024)Heterogeneous Face Recognition Using Domain Invariant UnitsICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447481(4780-4784)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447481
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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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MM '12

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SIGMM

MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Otroshi Shahreza HMarcel S(2024)Template Inversion Attack Using Synthetic Face Images Against Real Face Recognition SystemsIEEE Transactions on Biometrics, Behavior, and Identity Science10.1109/TBIOM.2024.33917596:3(374-384)Online publication date: Jul-2024
https://doi.org/10.1109/TBIOM.2024.3391759
George AMarcel S(2024)Modality Agnostic Heterogeneous Face Recognition with Switch Style Modulators2024 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB62174.2024.10744437(1-10)Online publication date: 15-Sep-2024
https://doi.org/10.1109/IJCB62174.2024.10744437
George AMarcel S(2024)Heterogeneous Face Recognition Using Domain Invariant UnitsICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447481(4780-4784)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447481
Shahreza HShkel YMarcel S(2024)On Measuring Linkability of Multiple Protected Biometric Templates Using Maximal LeakageIEEE Access10.1109/ACCESS.2024.343353612(106618-106630)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3433536
Tirronen SKadiri SAlku P(2024)The Effect of the MFCC Frame Length in Automatic Voice Pathology DetectionJournal of Voice10.1016/j.jvoice.2022.03.02138:5(975-982)Online publication date: Sep-2024
https://doi.org/10.1016/j.jvoice.2022.03.021
George AMarcel S(2024)Heterogeneous Face Recognition with Prepended Domain TransformersFace Recognition Across the Imaging Spectrum10.1007/978-981-97-2059-0_7(169-204)Online publication date: 17-May-2024
https://doi.org/10.1007/978-981-97-2059-0_7
Shahreza HMarcel SOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Face reconstruction from facial templates by learning latent space of a generator networkProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666679(12703-12720)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666679
Alishbayli ASchlegel NEnglitz B(2023)Using auditory texture statistics for domain-neutral removal of background soundsFrontiers in Audiology and Otology10.3389/fauot.2023.12269461Online publication date: 19-Sep-2023
https://doi.org/10.3389/fauot.2023.1226946
Shahreza HHahn VMarcel S(2023)MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron2023 31st European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO58844.2023.10289780(605-609)Online publication date: 4-Sep-2023
https://doi.org/10.23919/EUSIPCO58844.2023.10289780
Otroshi Shahreza HShkel YMarcel S(2023)Measuring Linkability of Protected Biometric Templates Using Maximal LeakageIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.326617018(2262-2275)Online publication date: 2023
https://doi.org/10.1109/TIFS.2023.3266170
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