Measuring the Rarity of Fingerprint Patterns in the Dutch Population Using an Extended Classification Set

Jongh, Arent de; Lubach, Anko R.; Kwie, Sheryl L. Lie; Alberink, Ivo

doi:10.1111/1556-4029.13838

Cited by 22 publications

(29 citation statements)

References 7 publications

(23 reference statements)

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“…There are three general types of fingerprints; loop, whorl, and arch. The whorl type occurs in about 25-35% of all fingerprints, see [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

“…• a critical point with an elliptic domain if b n ≠ 0, n is an odd number and n < m and also if b n ≠ 0, n is an odd number, n = m and ≥ 0, 2. Let k = 2m with m ≥ 1 in (2). Then the origin is • a cusp if b n = 0 and also if b n ≠ 0 and n ≥ m,…”

Section: Introductionmentioning

confidence: 99%

“…(Theorems 2 and 3, page 151[25]). Let k = 2m + 1 with m ≥ 1 in(2) and let = b2 n + 4(m + 1)a k .Then if a k > 0, the origin is a (topological) saddle. If a k < 0, the origin is • a focus or a center if b n = 0 and also if b n ≠ 0 and n > m or if n = m and < 0,…”

mentioning

confidence: 99%

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The Mathematical Model and Deep Learning Features Selection for Whorl Fingerprint Classifications

Jawarneh¹,

Alsharman²

2021

IJCIS

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In this paper, different classes of the whorl fingerprint are discussed. A general dynamical system with a parameter is created using differential equations to simulate these classes by varying the value of . The global dynamics is studied, and the existence and stability of equilibria are analyzed. The Maple is used to visualize fingerprint's orientation image as a smooth deformation of the phase portrait of a planar dynamical system. In general, the databases of fingerprint are not categorized to retained by artificial intelligence tools such Convolutional Neural Networks (CNNs) architectures, so finding a dynamical system to categorize fingerprint database of fingerprints images allows CNNs architectures to retrained with more accuracy. NIST Special Database (SD) 302d fingerprint dataset is retrained over VGG16 as CNN architecture.

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“…There are three general types of fingerprints; loop, whorl, and arch. The whorl type occurs in about 25-35% of all fingerprints, see [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Mathematical Model and Deep Learning Features Selection for Whorl Fingerprint Classifications

Jawarneh¹,

Alsharman²

2021

IJCIS

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“…A fingerprint possesses a large number of specific characteristics that assist with identifying an individual. Extensive works are available describing fingerprint pattern distribution [1][2][3][4][5][6][7][8], fingerprint ridge count [9][10][11][12], ridge density [13][14][15][16][17], minutiae [5,6], and other dermatoglyphic features present on the fingertips [18,19]. In a typical forensic case, obtaining a full ten digit set of fingerprints is highly unlikely.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint analysis for the determination of hand origin (right/left) using the axis slant in whorl patterns

Kapoor

Badiye

Mishra

2020

Forensic Sciences Research

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Fingerprints are frequently encountered during both civil and criminal investigations. Fingerprints possess numerous characteristics that assist with personal identification. Determining the hand of origin (right or left) for an individual fingerprint would help reduce investigation time and potentially eliminate certain suspects. In this study, we collected a total of 2 900 single digit fingerprints from 290 individuals, and the whorl axis slant was examined in the 743 whorl pattern fingerprints (385 from the right hand and 358 from the left hand). A slant towards the right side was present in 81.82% of samples from the right hand, while a slant towards the left side was observed in 80.73% of samples from the left hand. After applying a chi-square test to the dataset, the results were found to be statistically significant for the determination of hand origin. Our results suggest that the whorl axis slant in a fingerprint is indicative of hand origin (right or left). KEY POINTS Single digit fingerprints with whorl pattern were analyzed. Whorl "axis slant" was used to determine the hand origin. Right axis slant would indicate the right hand of the print. Left axis slant would indicate the left hand of the print.

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“…Another possibility is a holistic (non‐numerical) method that requires an expert to work with all the characteristics of the print, including the quality and quantity of these features. The required minimum quantity of characteristics may vary .…”

mentioning

confidence: 99%

Distribution of the Minutiae in Hypothenar Palm Prints in Slovak Adults: Indications for Personal Identification

Kondeková

Beňuš

Masnicová

et al. 2020

Journal of Forensic Sciences

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Minutiae are small distinguishing features found along every ridge flow, which make each friction ridge print unique. The most common friction ridge prints found at the crime scene are fingerprints; therefore, the most of the minutiae studies are focused exactly on this kind of prints. The authors believe that further examination and enlargement of the palm print database could result in better use of the palm prints for personal identification. We analyzed a total of 160 palm prints from 40 females and 40 males aged between 18 and 70 years from Slovakia. For the evaluation of the minutiae, the area of the hypothenar had to be marked out. The classification of the minutiae used for this study was based on a modified version of the classification system using the total of 13 types of minutiae. The frequency of every minutiae type was calculated and, using the chi‐square test with Yates's correction, bilateral and sex differences were assessed. The relationship between the different types of minutiae was examined with Pearson's correlation test. During the initial phases of the identification process, the focus should be on the least common types of minutiae (Y or M and return), which were found not to correlate; thus, their mutual occurrence is random (e.g., overlap—Y or M, crossbar—return, or Y or M—dock). The results of the present study show which specific minutiae types are the most suitable for personal identification. These findings may be beneficial in more effective outcome of the identification process.

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Measuring the Rarity of Fingerprint Patterns in the Dutch Population Using an Extended Classification Set

Cited by 22 publications

References 7 publications

The Mathematical Model and Deep Learning Features Selection for Whorl Fingerprint Classifications

The Mathematical Model and Deep Learning Features Selection for Whorl Fingerprint Classifications

Fingerprint analysis for the determination of hand origin (right/left) using the axis slant in whorl patterns

Distribution of the Minutiae in Hypothenar Palm Prints in Slovak Adults: Indications for Personal Identification

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