Gender Classification Based on Fingerprint Database Using Association Rule Mining

Mishra, Ashish; Dubey, Shivendu; Sahu, Amit

doi:10.1007/978-981-15-7234-0_10

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…Womenhave a considerably greater combined ridge density than men.By this proposed work 82.1 % of success rate is achieved. [4] (Ejiogu 2020)For improving the performance of gender classification using fingerprints the author has designed the fused combinations amongst the five right-hand finger types.CNN is used for the training purpose, the proposed architecture consists 20 layers comprising 5convolutional layers, 6 Rectified linear unit (RELU)layers, 5 max pooling layers, 2 fully connectedlayers, softmax and classification layers. Grayscale image is given as input to the system from c1 to c5 the convolution layersare presented with these numbers of activation layers 128,128, 128, 256 and 256, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Secondly, the right index fingerprint was selected and a 5mm 2-square section from upper left part of the core point was taken, the selected part was cropped and saved as a new image. This new image was analyzed, and the ridge thickness 1 , ridge counts 2 and average ridge thickness 3 features were found and recorded to find gender 4 . The Naive Bayes method is used for gender classification.…”

Section: Related Workmentioning

confidence: 99%

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review on identification of gender using fingerprints

Mishra¹,

Jain²

2022

ijhs

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Every person in the world has unique biometrics characteristics such as iris, face, voice, palm or finger-vein patterns, and fingerprints. Biometrics, such as fingerprints are even more distinctive than DNA. Although identical twins can share DNA, they cannot have identical fingerprints. The fingerprint impressions are created by using ridges and valleys which are present on the surface of fingers. Fingerprints help to afford an infallible means of personal identification because the ridge arrangement on each person's finger is unique and does not change with growth or age. Some studies in machine learning and data mining investigate a relationship between fingerprint and gender. Hereby using ridges present on the finger it can be identified that fingerprint is of male or female, as males have larger body size than females, the equal number of ridges on a larger surface area means males have a lower fingerprint ridge density; finding out the gender from fingerprints can reduce the search space to half. The criminal justice system uses fingerprints to authenticate a convicted offender's identification and track their previous arrests and convictions, criminal tendencies, known associates, and other important information in the absence of DNA.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

review on identification of gender using fingerprints

Mishra¹,

Jain²

2022

ijhs

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show abstract

“…On the contrary, researchers from the domain of computer science actively explored various machine learning algorithms in discriminating gender based on multiple features of fingerprint [16][17][18][19][20][21][22][23][24][25]. Compared to [11][12][13][14][15] performing manual calculation of number of ridge, [16][17][18][19][20][21][22][23][24][25] tended to extract fingerprint feature assisted by either image processing technique or digitizing the image of fingerprint. For instance, [20] proposed an image processing algorithm to determine the number of ridge.…”

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confidence: 99%

Forensic Fingerprint Analysis using Self-Organizing Maps, Classification and Regression Trees and Naïve Bayes Methods

Lee¹,

Bohari²,

Sanih³

et al. 2022

IJCDS

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Fingerprint is one type of physical evidence frequently encountered at a crime scene. It is useful in revealing identity of the culprit. However, poor quality latent fingerprint collected from a crime scene seldom makes an identification reliable. In practice, the identification is accomplished by matching a known print to an unknown print according to the types and locations of their minutiae features. When it is infeasible to conduct an identification, forensic scientist can attempt to predict the sex of donor of the latent fingerprint in order to narrow down the scope of searching of the suspect. In the context of forensic science, sexual dimorphism in ridge count has been studied for a few decades ago. Meanwhile, gender classification based on fingerprint images have been regularly reported in the field of computer science. Viewed from a practical perspective, image of a latent print collected from a real crime scene can be low in quality or even incomplete. Hence, this study has not considered image of fingerprint as input data but the number of diagonal ridge counted manually within a well-defined region, i.e. 25 centimeter squared. Firstly, the fingerprint data was explored using self-organizing maps method with respect to sexual dimorphism and ethnic difference. Next, Naïve Bayes (NB) and Classification and Regression Trees (CART) algorithms were, respectively, used to construct predictive model for discriminating gender based on the fingerprint data. A multitude of prediction models were constructed by considering tendigit, five-digit and one-digit samples, respectively, to predict gender by three races, i.e. Chinese, Indians and Malays; and the combined sub-population. Each of the models was validated using bootstrapping without replacement approach. Results showed that the single-digit samples produced accuracy rate slightly lower than that obtained using five-or ten-digit samples. Comparing to the global predictive model, ethnicity-specific models of Indian and Malay subjects showed slight improvement in external accuracy rate. Moreover, by considering all five digits of a particular hand as input data, NB tends to outperform CART. However, both NB and CART are comparable to each other when one-digit sample was considered as input data. In conclusion, SOM is useful for exploring sexual dimorphism of fingerprint data and NB outperforms CART in modelling of the fingerprint data.

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Gender Classification Based on Fingerprint Database Using Association Rule Mining

Cited by 2 publications

References 10 publications

review on identification of gender using fingerprints

review on identification of gender using fingerprints

Forensic Fingerprint Analysis using Self-Organizing Maps, Classification and Regression Trees and Naïve Bayes Methods

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