Analysis, Design and Implementation of Human Fingerprint Patterns System “Towards Age &amp; Gender Determination, Ridge Thickness To Valley Thickness Ratio (RTVTR) &amp; Ridge Count On Gender Detection

Omidiora, E. O.; Ojo, Olusola Samuel; Yekini, N A; Tubi, T.O.

doi:10.14569/ijarai.2012.010210

Cited by 12 publications

(4 citation statements)

References 13 publications

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“…Fingerprints of formed by genetic combination, environmental factors in human body. This is the reason even monozygotic twins have different fingerprints, 4 so here by we can tell that fingerprints establish a personal identification method and mostly used for tracking of criminals. Hence, the present study was aimed to find out the correlation between gender on both lip prints and finger prints.…”

Section: Introductionmentioning

confidence: 95%

A study on comparison of lip prints and finger prints based on gender and its reliability in forensic odontology: A cross-sectional study

Vengala¹,

Pimpley³

et al. 2023

IJFCM

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In an individual, fingerprints and lip prints are one of the vital parts in identifying the person in both civil and criminal cases because of their uniqueness. The aim of the study is to identify if any correlation between lip prints and finger and also based on gender wise.The present study was carried to determine the distribution of different patterns of both finger and lip prints. The study comprised of about 200 subjects, aged between 17 to 24 years. Impressions were collected using lip prints and ink stamp pad and pasted on filter paper and cellophane tape. Chi square test was used to find out any relation between lip prints and finger prints.

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Section: Introductionmentioning

confidence: 95%

A study on comparison of lip prints and finger prints based on gender and its reliability in forensic odontology: A cross-sectional study

Vengala¹,

Pimpley³

et al. 2023

IJFCM

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“…DWT is frequently used in fusion with singular value decomposition (SVD) [14] and with principal component analysis (PCA) [15]. Transform feature extraction is used for Fingerprint matching [13,16], but it has not yet been investigated for gender Identification. In this work, we compare FFT, PCA, and fused features for gender identification.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Based Gender Classification

2024

IRJMETS

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Male-female classification from a fingerprint is an important step in forensic science, anthropological and medical studies to reduce the efforts required for searching a person. The aim of this research is to establish a relationship between gender and the fingerprint using some special features such as ridge density, ridge thickness to valley thickness ratio (RTVTR) and ridge width. showed that male-female classification can be done correctly up to 88.5% based on white lines count, RTVTR & ridge count using Neural Network as Classifier. We have used RTVTR, ridge width and ridge density for classification and SVM as classifier. We have found male-female can be correctly classified up to 91%. Gender classification plays an active role in several applications such as biometrics, criminology, surveillance, human computer interaction, commercial profiling. Though biometric traits such as face, gait, iris and hand shape are used for gender classification in the past, majority of the work is based on face as it contains more prominent features than others. In this paper we have analyzed fingerprints for gender classification with a hope that it has great potential for future research. We have employed a three convolutional layer CNN with rectified linear and activation functions on NIST database which contains a set of 4000 images and achieved 99% accuracy. Performance of the proposed system demonstrated that fingerprints contains vital features to discriminate gender of a person. Humans have distinctive and unique traits which can be used to distinguish them thus, acting as a form of identification. Biometrics identify people by measuring some aspect of individual's anatomy or physiology such as hand geometry or fingerprint which consists of a pattern of interleaved ridges and valleys. The year 2015 election in Nigeria was greeted by some petitions including under-aged voters. The need for an age and gender detector system is a major concern for organizations at all levels where integrity of information cannot be compromised. This work developed a system that determines human age-range and gender using fingerprint analysis trained with Back Propagation Neural Network (for gender classification) and DWT+PCA (for age classification). A total of 280 fingerprint samples of people with various age and gender were collected. 140 of these samples were used for training the system"s Database; 70 males and 70 females respectively. This was done for age groups 1-10, 11-20, 21-30, 31-40, 41-50, 51-60 and 61-70 accordingly. In order to determine the gender of an individual, the Ridge Thickness Valley Thickness Ratio (RTVTR) of the person was put into consideration. Result showed 80.00 % classification accuracy for females and 72.86 % for males while 115 subjects out of 140 (82.14%) were correctly classified in age.

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“…Fingerprint is an impression left by the friction ridges of the finger tips (Cummins & Midlo, 1943). The formation of fingerprint ridges is under the influence of genetic and environmental factors (Babler, 1991;Omidiora et al, 2012). The variation in fingerprint ridge configuration is unique among individuals including identical twins (Anil et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Pattern of Fulani and Higgi ethnic groups in Michika LGA, Adamawa State, Nigeria

Atiku,

Tanko,

Adamu

et al. 2024

dujopas

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Developing countries experience surge of insurgency, banditry, cybercrime and other related violence. Criminals often go undetected because of poor forensic capabilities in human detection. Fingerprint features have been very effective in establishment of identity. The aim of this study was to determine fingerprint pattern of Fulani and Higgi ethnic groups of Michika, Adamawa State, Nigeria. A total of 5020 valid fingerprints patterns were captured using live fingerprint scanner. These included; 1230 Fulani male; 1250 Fulani female; 1280 Higgi male; 1260 Higgi female. SPSS version 26.0 statistical software was used for statistical analysis and P < 0.05 was set as a level of significance. Descriptive statistics were employed to determine the frequency of loops whorls and arches in all the fingers of both hands, both sexes and both ethnic groups. Loops were the most frequent pattern in all the observations followed by whorls, and then arches. However, there were relative differences in the percentage proportion across the digits, between left and right, male and female and Fulani and Higgi. The frequency of loop and whorl were equal in right ring (48.8 %) of Fulani male and right thumb (47.7 %) of Higgi male. The frequency of loop was less than that of whorl in left thumb (Loop: 39.8 %; Whorl: 48.8 %) and right thumb (Loop: 43.9 %; Whorl: 48.8 %) of Fulani male. Loops were more frequent in little fingers (85.1 %) followed by middle fingers (73.4 %) and least in thumbs (50.8 %) of left male Higgi; arches more frequent in left hand than right (11.4 % and 7.3 % in Fulani male thumbs respectively). These biological variations might suggest preferential selection of finger as potential predictors of other biological features such as blood group.

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Analysis, Design and Implementation of Human Fingerprint Patterns System “Towards Age & Gender Determination, Ridge Thickness To Valley Thickness Ratio (RTVTR) & Ridge Count On Gender Detection

Cited by 12 publications

References 13 publications

A study on comparison of lip prints and finger prints based on gender and its reliability in forensic odontology: A cross-sectional study

A study on comparison of lip prints and finger prints based on gender and its reliability in forensic odontology: A cross-sectional study

Fingerprint Based Gender Classification

Fingerprint Pattern of Fulani and Higgi ethnic groups in Michika LGA, Adamawa State, Nigeria

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