Gender inference within Turkish population by using only fingerprint feature vectors

Ceyhan, Eyüp Burak; Sağıroğlu, Şeref

doi:10.1109/cibim.2014.7015456

Cited by 9 publications

(17 citation statements)

References 10 publications

(22 reference statements)

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“…The research of gender classification based on fingerprint was conducted in some countries, such as in sub-Saharan region [11], Turkey [12], [13], Thai [14], Malaysia [15] and much more.…”

Section: Gender Classification Based On Ridge Densitymentioning

confidence: 99%

A Short Review of Gender Classification based on Fingerprint using Wavelet Transform

Suwarno¹,

Santosa²

2017

ijacsa

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Abstract-In some cases, knowing the gender of fingerprint owner found in criminal or disaster scene is advantageous. Theoretically, if the number of the male and female fingerprints in a database is equal, then the identification process of a fingerprint on that database would be two times faster. Some methods have been used to classify gender based on the fingerprint. Most of the method is based on ridge density. This method showed good result. However, it is sensitive to the location of the fingerprint area where ridge density is determined. This paper reviews some literature that used wavelet transform to generate features of a fingerprint. As far as what we found in literature, the number of papers on this topic is very limited. However, based on the literature we reviewed, wavelet transform gives some advantages compared to ridge density counting.

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“…The research of gender classification based on fingerprint was conducted in some countries, such as in sub-Saharan region [11], Turkey [12], [13], Thai [14], Malaysia [15] and much more.…”

Section: Gender Classification Based On Ridge Densitymentioning

confidence: 99%

A Short Review of Gender Classification based on Fingerprint using Wavelet Transform

Suwarno¹,

Santosa²

2017

ijacsa

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“…Based on previous research [3][4] [5][6] [7][8], ridge density is the criteria which is most prominent to differentiate the sex of a person [9]. From these studies, we can infer that ridge density is a good feature for identifying the gender.…”

Section: Introductionmentioning

confidence: 98%

“…The statistics of dermatologlyphics differ between gender, ethical groups, and age categories [1] [2]. These categories were defined during the early investigation of fingerprint structure by looking at the intensity of ridges and ridge count [3][4] [5][6] [7] [8].…”

Section: Introductionmentioning

confidence: 99%

“…Many research publication works discover and appraise the weakness of gender classification accuracy problem [10], while some researcher propose of a new classification methods for gender classification problem [11][12] [13] and several publications enhance the accuracy by comparing the classification rate using different classifier [3] [14].…”

Section: Introductionmentioning

confidence: 99%

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Development of a Fingerprint Gender Classification Algorithm Using Fingerprint Global Features

Abdullah¹,

Rahman²,

Abas³

et al. 2016

ijacsa

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Abstract-In forensic world, the process of identifying and calculating the fingerprint features is complex and take time when it is done manually using fingerprint laboratories magnifying glass. This study is meant to enhance the forensic manual method by proposing a new algorithm for fingerprint global feature extraction for gender classification. The result shows that the new algorithm gives higher acceptable readings which is above 70% of classification rate when it is compared to the manual method. This algorithm is highly recommended in extracting a fingerprint global feature for gender classification process.

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“…Fingerprints give some or more information about gender, origin, face sketch, age, etc. [18][19][20][21][22][23]26]. Ridge numbers and characteristics are generally worked on [9].…”

Section: Introductionmentioning

confidence: 99%

Parmak İzinden Cinsiyet Tahmininde 10 Parmak Arasındaki İlişkinin Analizi

Ceyhan¹,

Sağıroğlu²

2017

SAÜ Fen Bilimleri Enstitüsü Dergisi

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This study investigates the relationship among fingerprint and gender for all 10 fingerprints. 410 fingerprints taken from 19 females and 22 males aged between 18 and 25 years old were considered for this investigation. This is the first comprehensive study in literature that investigates 10 fingerprints for Turkish citizens. Ridge density, ridge thickness to valley thickness ratio and total ridge breadth values gained from our fingerprint database were used to determine gender. The results have shown that gender determination is successful for Turkish citizens with any of ten fingerprints. The average values for ridge density-ridge breadth-RTVTR are 13.09-36.56-0.46 for men and 14.43-37.44-0.47 for women, respectively. The gender difference of ridge density is determined as 1.34, which is the lowest value among the other studies in the literature.10 parmak öznitelikleri kullanılarak parmak izi ile cinsiyet arasındaki ilişkinin tespiti ÖZ Bu çalışma 10 parmak için parmak izi ve cinsiyet arasındaki ilişkiyi araştırmaktadır. Yaşları 18 ve 25 arasında olan 19 bayan ve 22 baydan alınan 410 parmak izi araştırma için değerlendirilmiştir. Bu çalışma Türk vatandaşlarının 10 parmak izini inceleyen literatürdeki ilk kapsamlı çalışmadır. Parmak izi veritabanımızdan elde edilen tepe yoğunluğu, tepe kalınlığının vadi kalınlığına oranı ve ortalama tepe genişliği değerleri cinsiyeti tespit etmek için kullanılmıştır. Sonuçlar, Türk vatandaşları için on parmaktan herhangi biri ile cinsiyet tespitinin başarılı olduğunu göstermektedir. Tepe kalınlığı-tepe genişliği-RTVTR için ortalama değerler baylar ve bayanlar için sırasıyla 13,09-36,56-0,46 ve 14,43-37,44-0,47'dir. Tepe yoğunluğunun cinsiyetler arasındaki farkı literatürdeki diğer çalışmalara göre en düşük değer olan 1,34 olarak tespit edilmiştir.

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Gender inference within Turkish population by using only fingerprint feature vectors

Cited by 9 publications

References 10 publications

A Short Review of Gender Classification based on Fingerprint using Wavelet Transform

A Short Review of Gender Classification based on Fingerprint using Wavelet Transform

Development of a Fingerprint Gender Classification Algorithm Using Fingerprint Global Features

Parmak İzinden Cinsiyet Tahmininde 10 Parmak Arasındaki İlişkinin Analizi

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