Gender determination from fingerprint patterns distribution among malaysian tamils

Moorthy, T. Nataraja; Rajathi, S.; Sairah, A. K.

doi:10.5958/0974-4614.2019.00009.3

Cited by 2 publications

(3 citation statements)

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“…Similar observations were reported in majority of the previous studies. [8][9][10][11][12][13][14][15][16][17][18][19][20][21] Females have thinner and narrower ridges creating more ridge density in a given area than the males. 12,13,17 The detailed mean ridge density and range of number of ridges of each finger (ROC and LOC) for females and males are tabulated in table 2 and table 3 respectively.…”

Section: Resultsmentioning

confidence: 99%

“…10 Investigation on gender determination through fingerprint ridge density was significantly conducted worldwide on people of various nationalities and ethnic groups. [11][12][13][14][15][16][17][18][19][20][21] However, the studies mostly included only a few fingers most commonly thumb and index fingers, though; it is possible to encounter the fingerprints of all the ten digits on the crime scene. Apart from thumb; index, middle and ring also fingers hold significant importance.…”

Section: Introductionmentioning

confidence: 99%

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Gender Determination Using Fingerprint Ridge Density in Perambalur District of Tamil Nadu

Reshma M,

Shanmathi K,

Jeevapriyadarshini J

et al. 2024

IJSRR

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Fingerprints, renowned for their individuality, serve as pivotal evidence in the criminal justice system. It aids investigators in bolstering criminal and civil investigations, given the permanence and distinctiveness of fingerprints. Fingerprints are also helpful in determination of gender in such cases which helps in narrowing down the scope of search. Numerous studies have been conducted on determination gender determination using fingerprint ridge density but no such work was found for Perambalur district, Tamil Nadu. Hence, the present study aimed to explore correlation between fingerprints and gender in the young population of Perambalur district, Tamil Nadu. A total of 600 rolled fingerprints of index, middle, and ring fingers of both the hands were collected from 50 males and 50 females participants. Ridge density was calculated in an area of 25 mm 2 . It was observed that ridge density in a given specific area was higher in females than in males. The findings add to the existing literature and aims to help the forensic investigations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gender Determination Using Fingerprint Ridge Density in Perambalur District of Tamil Nadu

Reshma M,

Shanmathi K,

Jeevapriyadarshini J

et al. 2024

IJSRR

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“…Related study on deep learning approach for gender classification was conducted and 62% performance accuracy on the level of a single minutia was obtained [27]. Also, a study on fingerprint pattern distribution in all ten fingers among Malaysian Tamils for gender determination for use in crime scene investigation [28], was carried out. The study used 280 adult Malaysian Tamils living mostly in Peninsular Malaysia.…”

Section: Introductionmentioning

confidence: 99%

Gender recognition based fingerprints using dynamic horizontal voting ensemble deep learning

Olufunso

Evwiekpaefe²,

Irhebhude³

2022

Int. J. Adv. Intell. Informatics

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Despite tremendous advancements in gender equality, there are still persistent gender disparities, especially in important human activities. Consequently, gender inequality and related concerns are serious problems in our global society. Major players in the global economy have identified the gender identity system as a crucial stepping stone for bridging the enormous gap in gender-based problems. Extensive research conducted by forensic scientists has uncovered a unique pattern in the fingerprint, and these distinguishing characteristics of fingerprints can be utilized to determine the gender of individuals. Numerous research has revealed various fingerprint-based approaches to gender recognition. This research aims to present a novel dynamic horizontal voting ensemble model with a hybrid Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) deep learning algorithm as the base learner to determine human gender attributes based on fingerprint patterns automatically. More than four thousand Live fingerprint images were acquired and subjected to training, testing, and classification using the proposed model. The results of this study indicated over 99% accuracy in predicting a person’s gender. The proposed model also performed better than other state-of-the-art models, such as ResNet-34, VGG-19, ResNet-50, and EfficientNet-B3, when implemented on the SOCOFing public dataset.

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Gender determination from fingerprint patterns distribution among malaysian tamils

Cited by 2 publications

References 0 publications

Gender Determination Using Fingerprint Ridge Density in Perambalur District of Tamil Nadu

Gender Determination Using Fingerprint Ridge Density in Perambalur District of Tamil Nadu

Gender recognition based fingerprints using dynamic horizontal voting ensemble deep learning

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