NIST special database 27:

Garris, Michael D.; McCabe, R Michael

doi:10.6028/nist.ir.6534

Cited by 86 publications

(14 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, we used a training set, a validation set, and a test set extracted from the NIST-3 database [8]. Both the training set and the validation set were made up of 10,000 associative pairs of segmented handwritten digits each, obtained from disjoint groups of writers, while the test set consisted of 5,000 independent digits.…”

Section: Resultsmentioning

confidence: 99%

Theoretical and experimental analysis of a two-stage system for classification

Giusti

Sperduti

2002

IEEE Trans. Pattern Anal. Machine Intell.

View full text Add to dashboard Cite

AbstractÐWe consider a popular approach to multicategory classification tasks: a two-stage system based on a first (global) classifier with rejection followed by a (local) nearest-neighbor classifier. Patterns which are not rejected by the first classifier are classified according to its output. Rejected patterns are passed to the nearest-neighbor classifier together with the topEh ranking classes returned by the first classifier. The nearest-neighbor classifier, looking at patterns in the topEh classes, classifies the rejected pattern. An editing strategy for the nearest-neighbor reference database, controlled by the first classifier, is also considered. We analyze this system, showing that even if the first level and nearest-neighbor classifiers are not optimal in a Bayes sense, the system as a whole may be optimal. Moreover, we formally relate the response time of the system to the rejection rate of the first classifier and to the other system parameters. The error-response time trade-off is also discussed. Finally, we experimentally study two instances of the system applied to the recognition of handwritten digits. In one system, the first classifier is a fuzzy basis functions network, while in the second system it is a feed-forward neural network. Classification results as well as response times for different settings of the system parameters are reported for both systems.

show abstract

Section: Resultsmentioning

confidence: 99%

Theoretical and experimental analysis of a two-stage system for classification

Giusti

Sperduti

2002

IEEE Trans. Pattern Anal. Machine Intell.

View full text Add to dashboard Cite

show abstract

“…To overcome this, Gabor filters (Gao et al, 2010;Yoon et al, 2011) are used to enhance the latent images and in turn, overcome the influence of background noises. These methods perform better than (Jain et al, 1997) but are only able to FIGURE 1 | Examples of low-quality latent fingerprints from the NIST SD27 (Garris and Mccabe, 2000) database (latent fingerprints at the top and their respective true-mates at the bottom) (A) Original latent classified as good and its true mate (B) Original latent classified as bad and its true mate (C) Original latent classified as ugly and its true mate. extract low-level ridge features with the help of handcrafted methods.…”

Section: Literature Surveymentioning

confidence: 99%

End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

Deshpande¹,

Malemath

Patil

et al. 2020

Front. Robot. AI

View full text Add to dashboard Cite

Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

show abstract

“…Two datasets were used for the investigations. One dataset consisted of fingerprint impressions 1 to 12 of the NIST Special Database SD27 [11]. This subset of the SD27 dataset consisted of 2484 images scanned from standard FBI fingerprint 10-print cards at 1000 ppi.…”

Section: Image Datamentioning

confidence: 99%