SVM Based Gender Classification Using Iris Images

Bansal, Atul; Agarwal, Ravinder; Sharma, Rajendra K.

doi:10.1109/cicn.2012.192

Cited by 38 publications

(25 citation statements)

References 15 publications

(20 reference statements)

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“…A person's gender is a soft biometric attribute that, in association with an individual, can be useful for recognition of that particular individual. Gender prediction based on characteristics of the iris is an idea that has been tried during the last decade with results varying from random accuracy up to 97% [17,9,1,15,4,16,2,8,13,14,3,10]. Most of the works used Support Vector Machines (SVM) as classifiers, with different approaches for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

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Predicting Gender From Iris Texture May Be Harder Than It Seems

Kuehlkamp

Bowyer

2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Predicting gender from iris images has been reported by several researchers as an application of machine learning in biometrics. Recent works on this topic have suggested that the preponderance of the gender cues is located in the periocular region rather than in the iris texture itself. This paper focuses on teasing out whether the information for gender prediction is in the texture of the iris stroma, the periocular region, or both. We present a larger dataset for gender from iris, and evaluate gender prediction accuracy using linear SVM and CNN, comparing hand-crafted and deep features. We use probabilistic occlusion masking to gain insight on the problem. Results suggest the discriminative power of the iris texture for gender is weaker than previously thought, and that the gender-related information is primarily in the periocular region.

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Section: Related Workmentioning

confidence: 99%

“…Another work that employed SVM with hand-crafted features [1], seems to have obtained better performance, reporting 83% accuracy. A similar result was presented by [15], in the work that originated the Gender-From-Iris dataset.…”

Section: Related Workmentioning

confidence: 99%

Predicting Gender From Iris Texture May Be Harder Than It Seems

Kuehlkamp

Bowyer

2019

2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

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“…They considered both race-fromiris and gender-from-iris, and their classification accuracy on gender-from-iris ranged from 47% to 62%. A similar approach was used by [1], which used 2D Discrete Wavelet Table 1: Overview of gender prediction from iris images.…”

Section: Related Workmentioning

confidence: 99%

Gender-from-Iris or Gender-from-Mascara?

Kuehlkamp

Becker

Bowyer

2017

2017 IEEE Winter Conference on Applications of Computer Vision (WACV)

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Predicting a person's gender based on the iris texture has been explored by several researchers. This paper considers several dimensions of experimental work on this problem, including person-disjoint train and test, and the effect of cosmetics on eyelash occlusion and imperfect segmentation. We also consider the use of multi-layer perceptron and convolutional neural networks as classifiers, comparing the use of data-driven and hand-crafted features. Our results suggest that the gender-from-iris problem is more difficult than has so far been appreciated. Estimating accuracy using a mean of N person-disjoint train and test partitions, and considering the effect of makeup -a combination of experimental conditions not present in any previous work -we find a much weaker ability to predict genderfrom-iris texture than has been suggested in previous work.

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“…An optimally biased predictor can be produced if there is an overlap of subjects in the training and test sets as indicated in [28], [29]. While most recent work in attribute prediction from iris have clearly adopted a subject-disjoint protocol [28], [29], [30], [31], some of the earlier papers on this topic have been ambiguous on this front [32], [33], [34], [35]. Table II and Table IV, respectfully, summarize the previous work on gender and race prediction from a single NIR image.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Covariate Influence on Gender and Race Prediction From Near-Infrared Ocular Images

Bobeldyk

Ross

2019

IEEE Access

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Recent research has explored the possibility of automatically deducing information such as gender, age and race of an individual from their biometric data. While the face modality has been extensively studied in this regard, the iris modality less so. In this paper, we first review the medical literature to establish a biological basis for extracting gender and race cues from the iris. Then, we demonstrate that it is possible to use simple texture descriptors, like BSIF (Binarized Statistical Image Feature) and LBP (Local Binary Patterns), to extract gender and race attributes from an NIR ocular image used in a typical iris recognition system. The proposed method predicts gender and race from a single eye image with an accuracy of 86% and 90%, respectively. In addition, the following analysis are conducted: (a) the role of different parts of the ocular region on attribute prediction; (b) the influence of gender on race prediction, and vice-versa; (c) the impact of eye color on gender and race prediction; (d) the impact of image blur on gender and race prediction; (e) the generalizability of the method across different datasets; and (f) the consistency of prediction performance across the left and right eyes.

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SVM Based Gender Classification Using Iris Images

Cited by 38 publications

References 15 publications

Predicting Gender From Iris Texture May Be Harder Than It Seems

Predicting Gender From Iris Texture May Be Harder Than It Seems

Gender-from-Iris or Gender-from-Mascara?

Analyzing Covariate Influence on Gender and Race Prediction From Near-Infrared Ocular Images

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