CVBL IRIS Gender Classification Database Image Processing and Biometric Research, Computer Vision and Biometric Laboratory (CVBL)

Aryanmehr, Saeed; Karimi, Mohsen; Boroujeni, Farsad Zamani

doi:10.1109/icivc.2018.8492757

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…In this section, the proposed method's efficiency is evaluated using the CVBL data set (Aryanmehr et al, 2018). The achieved results are compared to three recently reported methods using CVBL data set: (a) Deep belief network for fast fine‐tuning of the Layer Network (MLP) in the semi‐supervised step (Tapia & Aravena, 2017); (b) Convolutional neural network with three convolutional layers and two fully connected layers and a few neurons in the supervised step (Tapia & Aravena, 2017); (c) Convolutional Neural Network and MLP (Kuehlkamp et al, 2017).…”

Section: Discussion and Comparisonmentioning

confidence: 99%

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Authentication through gender classification from iris images using support vector machine

Khan

Doosti²,

Karimi

et al. 2021

Microscopy Res & Technique

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Soft biometric information, such as gender, iris, and voice, can be helpful in various applications, such as security, authentication, and validation. Iris is secure biometrics with low forgery and error rates due to its highly certain features are being used in the last few decades. Iris recognition could be used both independently and in part for secure recognition and authentication systems. Existing iris‐based gender classification techniques have low accuracy rates as well as high computational complexity. Accordingly, this paper presents an authentication approach through gender classification from iris images using support vector machine (SVM) that has an excellent response to sustained changes using the Zernike, Legendre invariant moments, and Gradient‐oriented histogram. In this study, invariant moments are used as feature extraction from iris images. After extracting these descriptors' attributes, the attributes are categorized through keycode fusion. SVM is employed for gender classification using a fused feature vector. The proposed approach is evaluated on the CVBL data set and results are compared in state of the art based on local binary patterns and Gabor filters. The proposed approach came out with 98% gender classification rate with low computational complexity that could be used as an authentication measure.

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Section: Discussion and Comparisonmentioning

confidence: 99%

“…Iris normalization (Aryanmehr, Karimi, & Boroujeni, 2018): (a) iris cut with Image J, (b) zonal iris (cartesian space), (c) normalized iris (polar space)…”

Section: Proposed Approach For Gender Classificationmentioning

confidence: 99%

Authentication through gender classification from iris images using support vector machine

Khan

Doosti²,

Karimi

et al. 2021

Microscopy Res & Technique

Self Cite

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“…Additionally, a comparative investigation of offthe-self textural descriptors and human ability in gender classification was undertaken. Moreover, Aryanmehr et al (2018), with Computer Vision and Biometric Laboratory (CVBL) introduced image processing and biometric research using the IRIS gender identification database. They created an iris image database for gender classification and evaluated it using a new gender classification algorithm.…”

Section: Literatures Reviewmentioning

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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“…The classification of gender from biometric traits is one of the important steps in forensic anthropology, which is used to identify the gender of a criminal in order to minimize the list of suspects in a search. Very few researchers have worked on gender classification using fingerprints [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15], Iris [16,17,18,19,54], palmprint [20,21,22,24,57,58],face [23,26,27,28,29,30,31,32,33,53,55,59,60], speech [34,35,36,37,38] and gait [42] etc and have gained the competitive results. These biometric traits which rely on any single biometric identifier often do not meet the requirements prudently as any uni-modal biometric system suffers from a variety of problems including distorted data, intra-class variations, inter-user similarity, constrained level of freedom, noncomprehensiveness, spoofs, Obfuscation (masking one'...…”

Section: Introductionmentioning

confidence: 99%

Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

Patil¹,

Kruthi²,

Gornale³

2019

IJIGSP

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A certain number of researchers have utilized uni-modal bio-metric traits for gender classification. It has many limitations which can be mitigated with inclusion of multiple sources of biometric information to identify or classify user's information. Intuitively multimodal systems are more reliable and viable solution as multiple independent characteristics of modalities are fused together. The objective of this work is inferring the gender by combining different biometric traits like face, iris, and fingerprints of same subject. In the proposed work, feature level fusion is considered to obtain robustness in gender determination; and an accuracy of 99.8% was achieved on homologous multimodal biometric database SDUMLA-HMT (Group of Machine Learning and Applications, Shandong University). The results demonstrate that the feature level fusion of Multimodal Biometric system greatly improves the performance of gender classification and our approach outperforms the state-of-the-art techniques noticed in the literature.

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CVBL IRIS Gender Classification Database Image Processing and Biometric Research, Computer Vision and Biometric Laboratory (CVBL)

Cited by 11 publications

References 8 publications

Authentication through gender classification from iris images using support vector machine

Authentication through gender classification from iris images using support vector machine

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

Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images

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