"The friction ridge pattern is a 3D structure which, in its natural state, is not deformed by contact with a surface''. Building upon this rather trivial observation, the present work constitutes a first solid step towards a paradigm shift in fingerprint recognition from its very foundations. We explore and evaluate the feasibility to move from current technology operating on 2D images of elastically deformed impressions of the ridge pattern, to a new generation of systems based on full-3D models of the natural nondeformed ridge pattern itself.
The present data release contains the data of 2 subjects of the 3D-FLARE DB.
These data is released as a sample of the complete database as these 2 subjects gave their specific consent for the distribution of their 3D fingerprint samples.
The acquisition system and the database are described in the article:
[ART1] J. Galbally, L. Beslay and G. Böstrom, "FLARE: A Touchless Full-3D Fingerprint Recognition System Based on Laser Sensing", IEEE ACCESS, vol. 8, pp. 145513-145534, 2020.
We refer the reader to this article for any further details on the data.
This sample release contains the next folders:
- 1_rawData: it contains the 3D fingerprint samples as they were captured by the sensor describe in [ART1], with no processing. This folder includes the same 3D fingerprints in two different formats:
* MATformat: 3D fingerprints in MATLAB format
* PLYformat: 3D fingerprints in PLY format
- 2_processedData: it contains the 3D fingerprint samples after the two initial processing steps carried out before using the samples for recognition purposes. These files are in MATLAB format. This folder includes:
* 2a_Segmented: 3D fingerprints after being segemented according to the process described in Sect. V of [ART1]
* 2b_Detached: 3D fingerprints after being detached according to the process described in Sect. VI of [ART1]
The naming convention of the files is as follows: XXXX_AAY_SZZ
XXXX: 4 digit identifier for the user in the database
AA: finger identifier, it can take values: LI (Left Index), LM (Left Middle), RI (Right Index), RM (Right middle)
Y: sample number, with values 0 to 4
ZZ: acquisition speed, it can take values 10, 30 or 50 mm/sec
With the data files we also provide a series of example MATLAB scripts to visualise the 3D fingerprints:
We cannot guarantee the correct functioning of these scripts depending on the MATLAB version you are running.
Two videos of the 3D fingerprint scanner can be checked at:
Dataset of fluorescent mice brain vessels Confocal 3D volumes aligned to Light-Field images.
- Single volume dimension: 1287x1287x64.
- Number of samples: 362
- Voxel size: 0.086x0.086x0.9 um.
- Objective: 40x/1.3 Oil.
- Stain: tomato lectin (DyLight594 conjugated, DL-1177, Vector Laboratories).
We introduce HUMAN4D, a large and multimodal 4D dataset that contains a variety of human activities simultaneously captured by a professional marker-based MoCap, a volumetric capture and an audio recording system. By capturing 2 female and 2 male professional actors performing various full-body movements and expressions, HUMAN4D provides a diverse set of motions and poses encountered as part of single- and multi-person daily, physical and social activities (jumping, dancing, etc.), along with multi-RGBD (mRGBD), volumetric and audio data. Despite the existence of multi-view color datasets c
* At this moment, the paper of this dataset is under review. The dataset is going to be fully published along with the publication of the paper, while in the meanwhile, more parts of the dataset will be uploaded.
The dataset includes multi-view RGBD, 3D/2D pose, volumetric (mesh/point-cloud/3D character) and audio data along with metadata for spatiotemporal alignment.
The full dataset is splitted per subject and per activity per modality.
There are also two benchmarking subsets, H4D1 for single-person and H4D2 for two-person sequences, respectively.
The fornats are:
- mRGBD: *.png
- 3D/2D poses: *.npy
- volumetric (mesh/point-cloud/): *.ply
- 3D character: *.fbx
- metadata: *.txt, *.json
Nextmed project is a software platform for the segmentation and visualization of medical images. It consist on a series of different automatic segmentation algorithms for different anatomical structures and a platform for the visualization of the results as 3D models.
This dataset contains the .obj and .nrrd files that correspond to the results of applying our automatic lung segmentation algorithm to the LIDC-IDRI dataset.
This dataset relates to 718 of the 1012 LIDC-IDRI scans.
The file consists in a folder for each result whith the .obj and .nrrd files generated by the Nextmed algorithms.