NIST Special Database 302 has been split into several parts from SD302a to SD302i. Our focus is on NIST SD302b and NIST SD302h exclusively.
The SD 302b directory structure is organized as follows:
images
- baseline # Collection type. Contain device code R, S, U, and V.
- R # Device code
- 1000 # Resolution
- slap # Impression type
- png # Image format
- slap-segmented
- ...
- 500
- ...
- ...
Count the subjects for each device.
We explore the subject in the database by counting the subject or person by uisng count_subject.py
Image names are in the form SUBJECT_ACTIVITY_HAND_ENCOUNTER_TECHNIQUE_DIGITIZER_RESOLUTION_DEPTH_CHANNELS_LPNUMBER_SOURCE.EXT
Device Code | Number of subjects |
R | 92 |
S | 108 |
R and S | 200 |
U | 200 |
V | 200 |
The SD 302h directory structure is organized as follows:
ebts # Record format
- latent # Collection type
- lffs # Transaction type
- original
- 1000 # Resolution, in PPI
- enhanced
- ...
- checksum_latent_lffs_enhanced.csv # File checksums
- checksum_latent_lffs_original.csv
In data_filter.ipynb, We perform filtering on latent fingerprint images from NIST SD302h using finger position labels from finger_positions.csv
, considering corresponding mates present in NIST SD302b (devices: U and V)
The LFFS format includes Field Number 13.999
, indicating a LATENT FRICTION RIDGE IMAGE. We read LFFS files starting from 13.999
until IEND
flag. These contents are passed into an io.BytesIO
object created from the encoded image's bytes. We utilize Image.open
from the PIL library to open and save the image into files. The source code for this process is available in lffs_to_image.py.
-
Special Publication (NIST SP) - 500-290e3
- [NIST.SP.500-290e3.pdf] (https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.500-290e3.pdf)
If you are utilizing the source code from this repository in your research, please reference the following.
If you have any questions or need assistance, reach us at supakit.kr@gmail.com.