Examination of Downsampling Strategies for Converting 1000 ppi Fingerprint Imagery to 500 ppi

Abstract

The Federal Bureau of Investigation (FBI) is increasing the sample rate at which it acquires and stores fingerprint imagery, and to accommodate the higher sample rate is replacing Wavelet Scalar Quantization (WSQ) with JPEG2000 as its standard compression algorithm. Currently the bulk of fingerprint data in operations is captured, processed and stored at 500 ppi (pixels per inch) using the WSQ compressed digital format. With the transition to 1000 ppi, some systems will unavoidably contain an overlap between 500 ppi and 1000 ppi operational pathways. This overlap may be a result of legacy infrastructure limitations, or some other financial or logistical reason. Additionally, there will still be a need to compare newly collected 1000 ppi images against legacy 500 ppi images, for both one-to-one and one-to-many scenarios. To create a bridge between legacy and modern data there needs to be a pathway for interoperability putting legacy and modern data on equal footing by converting one of the images to the same resolution as the other. Downsampling of the higher resolution 1000 ppi imagery to 500 ppi provides this pathway. The study compares several computational methods for downsampling of modern fingerprint images from 1000 ppi to 500 ppi. These treatments include pixel averaging, decimation, transcoding directly from the JPEG2000 codestream, Gaussian filtering and spectral truncation. Fidelity of downsampled 1000 ppi images relative to corresponding prints scanned natively at 500 ppi is evaluated via scoring by expert fingerprint examiners, an automated machine matcher, RMS differences and correlation of spectra. Gaussian low-pass filtering (σ = 0.8475, r = 4) combined with decimation emerged as the optimal downsampling strategy with respect to image fidelity when gauged by several different ranking strategies. Transcode methods involving selective decoding of JPEG2000 images so as to discard the highest resolution decomposition level yielded 500 ppi images that were not of optimal quality but were processed at considerably higher speed due to greater computational efficiency of such transcode algorithms making these methods attractive in a tradeoff between image fidelity vs. sheer computational throughput. EXECUTIVE SUMMARYThe criminal justice community has traditionally exchanged and stored fingerprint imagery data at 500 pixels per inch 1 (ppi) or 19.7 pixels per millimeter (ppmm). The Wavelet Scalar Quantization (WSQ) fingerprint image compression algorithm is currently the standard for the lossy compression of 500 ppi fingerprint imagery and the WSQ Gray-Scale Fingerprint Image Compression Specification [WSQ] provides guidance based on an International Association for Identification (IAI) study [FITZPATRICK] to establish the acceptable amount of fidelity loss due to lossy compression in order for a WSQ encoder and decoder to meet FBI certifications. These certifications are designed to ensure adherence to the WSQ specification and thereby to ensure fidelity and admissibility in courts of law for im...

NIST Special Database 302:

“…To ensure the veracity of recorded N2N finger positions in the baseline datasets, Challenge test staff also captured plain fingerprint impressions in a 4-4-2 slap configuration. This capture method refers to simultaneously imaging the index, middle, ring, and little fingers on the right hand (4), then repeating the process on the left hand (4), and finishing with the simultaneous capture of the left and right thumbs (2). This technique is a best practice to ensure finger sequence order, since it is physically challenging for a study participant to change the ordering of fingers when imaging them simultaneously.…”

NIST special database 301: nail to nail fingerprint challenge dry run

“…To ensure the veracity of recorded N2N fnger positions in the baseline datasets, Challenge test sta˙also captured plain fngerprint impressions in a 4-4-2 slap confguration. This capture method refers to simultaneously imaging the index, middle, ring, and little fngers on the right hand (4), then repeating the process on the left hand (4), and fnishing with the simultaneous capture of the left and right thumbs (2). This technique is a best practice to ensure fnger sequence order, since it is physically challenging for a study participant to change the ordering of fngers when imaging them simultaneously.…”

“…In the single day of the dry run, three rounds of 17 study participants had data captured. Tables [2][3][4] shows the friction ridge, face, and iris capture technologies used during the N2N Fingerprint Challenge dry run data collection. Plain, rolled, and touch-free impression fngerprints were captured from a multitude of devices, as well as a set of plain palm impressions.…”

“…A cursory overview of the observed fngerprint quality from devices dryrun-A through dryrun-P are provided in Figs. [1][2][3] and Table 5. Fig.…”

“…All devices operated at 196.85 PPCM (500 PPI), except for dryrun-H (393.7 PPCM or 1 000 PPI) and dryrun-P (unknown). Properly downsampled versions[2] of dryrun-H's images at 196.85 PPCM (500 PPI) are provided to maximize compatibility with algorithms designed around that resolution.Two devices captured multiple encounters of the study participants during their collection time. dryrun-H captured images of traditional identifcation fats (FRGPs 13 to 15) and upper palms (FRGPs 26 and 28).…”