Descriptor: Smart Event Face Dataset (SEFD)
dc.contributor.author | Islam, Riadul | |
dc.contributor.author | Tummala, Sri Ranga Sai Krishna | |
dc.contributor.author | Mule, Joey | |
dc.contributor.author | Kankipati, Rohith | |
dc.contributor.author | Jalapally, Suraj Kumar | |
dc.contributor.author | Challagundla, Dhandeep | |
dc.contributor.author | Howard, Chad | |
dc.contributor.author | Robucci, Ryan | |
dc.date.accessioned | 2025-04-23T20:31:19Z | |
dc.date.available | 2025-04-23T20:31:19Z | |
dc.date.issued | 2024-10-23 | |
dc.description.abstract | Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies. Neuromorphic imager variants, including event-based sensors, produce various representations such as streams of pixel addresses representing time and locations of intensity changes in the focal plane, temporal-difference data, data sifted/thresholded by temporal differences, image data after applying spatial transformations, optical flow data, and/or statistical representations. To address the critical barrier to entry, we provide an annotated, temporal-threshold-based vision dataset specifically designed for face detection tasks derived from the same videos used for Aff-Wild2. By offering multiple threshold levels (e.g., 4, 8, 12, and 16), this dataset allows for comprehensive evaluation and optimization of state-of-the-art neural architectures under varying conditions and settings compared to traditional methods. The accompanying tool flow for generating event data from raw videos further enhances accessibility and usability. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds, enabling more accurate and efficient object detection and localization and ultimately promoting the broader adoption of low-power, neuromorphic imaging technologies. IEEE SOCIETY/COUNCIL Signal Processing Society (SPS) DATA TYPE/LOCATION Images; MD, USA DATA DOI/PID 10.21227/bw2e-dj78 | |
dc.description.sponsorship | This work was supported in part by the National Science Foundation (NSF) under Grant 2138253, in part by the Maryland Industrial Partnerships (MIPS) program under Grant MIPS0012, and in part by the UMBC Startup grant | |
dc.description.uri | https://ieeexplore.ieee.org/abstract/document/10732017 | |
dc.format.extent | 8 pages | |
dc.genre | journal articles | |
dc.identifier | doi:10.13016/m2qxw5-9twc | |
dc.identifier.citation | Islam, Riadul, Sri Ranga Sai Krishna Tummala, Joey Mulé, Rohith Kankipati, Suraj Jalapally, Dhandeep Challagundla, Chad Howard, and Ryan Robucci. “Descriptor: Smart Event Face Dataset (SEFD).” IEEE Data Descriptions 1 (2024): 33–40. https://doi.org/10.1109/IEEEDATA.2024.3485026. | |
dc.identifier.uri | https://doi.org/10.1109/IEEEDATA.2024.3485026 | |
dc.identifier.uri | http://hdl.handle.net/11603/38038 | |
dc.language.iso | en_US | |
dc.publisher | IEEE | |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Student Collection | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department | |
dc.rights | Attribution 4.0 International CC BY 4.0 Deed | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/deed.en | |
dc.subject | UMBC Covail Lab | |
dc.subject | sparse vision | |
dc.subject | Location awareness | |
dc.subject | convolutional neural network (CNN) | |
dc.subject | Intelligent sensors | |
dc.subject | Heuristic algorithms | |
dc.subject | Sensors | |
dc.subject | Voltage control | |
dc.subject | Face detection | |
dc.subject | DVS | |
dc.subject | Event detection | |
dc.subject | Data models | |
dc.subject | UMBC Multi-Scale Thermal Transport Research Lab | |
dc.subject | face detection | |
dc.subject | Convolutional neural networks | |
dc.subject | UMBC VLSI-SOC GROUP | |
dc.subject | Videos | |
dc.subject | Aff-Wild | |
dc.subject | UMBC Cybersecurity Institute | |
dc.subject | Computer architecture | |
dc.subject | face dataset | |
dc.title | Descriptor: Smart Event Face Dataset (SEFD) | |
dc.type | Text | |
dcterms.creator | https://orcid.org/0000-0002-4649-3467 | |
dcterms.creator | https://orcid.org/0009-0002-8522-0872 | |
dcterms.creator | https://orcid.org/0009-0003-9737-2782 | |
dcterms.creator | https://orcid.org/0000-0001-7491-1710 | |
dcterms.creator | https://orcid.org/0009-0008-4077-0313 |
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