Dactyloscopy Based Gender Classification Using Machine Learning

Rekha, V. Smrithi; Gurupriya, S; Gayadhri, S; S, Sowmya Kamath

doi:10.1109/icscan.2019.8878756

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…There are no edge effects characteristic of empirical deformation models based on a thin metal plate2 or the Cappelli et al, 3 model. Also, the advantages of the model include the possibility of calculating the relative strain for arbitrary dactyloscopy images for which the corresponding reference points can be found, and for presenting various fingerprints [18]. The conducted statistical analysis also confirms that the proposed elastic model significantly improves the accuracy of image guidance in comparison with known models.…”

Section: Resultssupporting

confidence: 55%

Development modeling methods of analysis and synthesis of fingerprint deformations images

AlKaraawi

Dhahir

Alameri³

et al. 2020

IJECE

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The current study is to develop modeling methods, Analysis and synthesis of fingerprints deformations images and their application in problems of automatic fingerprint identification. In the introduction justified urgency of the problem, is given a brief description of thematic publications. In this study will review of modern technologies of biometric technologies and methods of biometric identification, the review of fingerprint identification systems, investigate for distorting factors. The influence of deformations is singled out, the causes of deformation of fingerprints are analyzed. The review of modern ways of the account and modeling of deformations in problems of automatic fingerprint identification is given. The scientific novelty of the work is the development of information technologies for the analysis and synthesis of deformations of fingerprint images. The practical value of the work in the application of the developed methods, algorithms and information technologies in fingerprints identification systems. In addition, it has been found that our paper "devoted to research methods and synthesis of the fingerprint deformations" is a more appropriate choice than other papers.

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Section: Resultssupporting

confidence: 55%

Development modeling methods of analysis and synthesis of fingerprint deformations images

AlKaraawi

Dhahir

Alameri³

et al. 2020

IJECE

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“…Males tend to have thicker ones that rest beneath the orbital rim, while females have higher ones that are arched. The distance between the brows is narrower in men and wider in women (Rekha et al, 2019) giving the former the impression of larger eyes. Basic procedures for iris-based gender classification are as follows.…”

Section: Introductionmentioning

confidence: 99%

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Hasan

Mstafa

2022

SJUOZ

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Gender classification is attractive in a range of applications, including surveillance and monitoring, corporate profiling, and human-computer interaction. Individuals' identities may be gleaned from information about their gender, which is a kind of soft biometric. Over the years, several methods for determining a person's gender have been devised. Some of the most well-known ones are based on physical characteristics like face, fingerprint, palmprint, DNA, ears, gait, and iris. On the other hand, facial features account for the vast majority of gender classification methods. Also, the iris is a significant biometric trait, because the iris, according to research, remains basically constant during an individual's life. Besides that, the iris is externally visible and is non-invasive to the user, which is important for practical applications. Furthermore, there are already high-quality methods for segmenting and encoding iris images, and the current methods facilitate selecting and extracting attribute vectors from iris textures. This study discusses several approaches to determining gender. The previous works of literature are briefly reviewed. Additionally, there are a variety of methodologies for different steps of gender classification. This study provides researchers with knowledge and analysis of the existing gender classification approaches. Also, it will assist researchers who are interested in this specific area, as well as highlight the gaps and challenges in the field, and finally provide suggestions and future paths for improvement.

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“…Fingerprint gender identification aims to extract gender-related features from an unidentified fingerprint to recognize one's gender information. It can be divided into two stages, namely extracting as well as classifying [1][2][3][4][5][6][7][8], in which the former step is of great significance since the effectiveness of gender identification, is primarily determined by the sufficiency of gender-related features. Nowadays, classifying ridge-related features extracted manually has achieved fairly good results, reaching an overall accuracy for 90% for average [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Research on Gender-related Fingerprint Features, Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification

Qiu

Lin

et al. 2022

Preprint

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Background: Fingerprint is an important biological feature of human body, which contains abundant biometric information.At present, the academic exploration of fingerprint gender characteristics is generally at the level of understanding, and the standardization research is quite limited. Methods: A robust approach is presented in this article, Dense Dilated Convolution ResNet Autoencoder, to extract valid gender information from fingerprints. By replacing the normal convolution operations with the atrous convolution in the backbone, prior knowledge is provided to keep the edge details and the global reception field can be extended. Results: The results were explored from three aspects: 1) The efficiency of the DDC-ResNet. 6 typical automatic feature extraction methods with 9 mainstream classifiers for a total of 54 combinations are evaluated on our dataset and provide unbiased experimental details. Experimental results demonstrate that the combination of our approach outperforms other combinations in terms of average accuracy and separate-gender accuracy. It reaches 96.5% for average and 0.9752(males)/0.9548(females) for separate-gender accuracy. 2) The effect of fingers. It is found that the best performance of classifying gender with separate fingers is achieved by the right ring finger. 3) The effect of specific features. Based on the observations of the concentrations of fingerprints visualized by our approach, it can be inferred that loops and whorls (level 1), bifurcations(level 2), as well as line shapes (level 3) are connected with gender. Finally, we will open source the dataset that contains 6000 fingerprint images. Conclusions: The results are demonstrated that autoencoder networks are a powerful method for extracting gender-specific features to help hide the privacy information of the user’s gender contained in the fingerprint. Our experiments also identified three levels of features in fingerprints that are important for gender differentiation, including loops and whorls shape, bifurcations shape, and line shapes.

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Dactyloscopy Based Gender Classification Using Machine Learning

Cited by 7 publications

References 11 publications

Development modeling methods of analysis and synthesis of fingerprint deformations images

Development modeling methods of analysis and synthesis of fingerprint deformations images

A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

Research on Gender-related Fingerprint Features, Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification

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