Iris or Periocular? Exploring Sex Prediction from Near Infrared Ocular Images

Bobeldyk, Denton; Ross, Arun

doi:10.1109/biosig.2016.7736928

Cited by 28 publications

(37 citation statements)

References 12 publications

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“…There is a substantial difference between the accuracy of periocular and normalized images. This difference confirms the findings of [2] and [8], which suggest the majority of the cues used in the prediction comes from the periocular region, instead of the iris texture. It can also be observed here that the same drop in accuracy happens when cosmetic images are removed from training, regardless of the type of image that is being used (periocular or normalized).…”

Section: Vgg Features With Svm Classifiersupporting

confidence: 87%

“…This could cause problems for cases where the iris is not centered in the image. Nevertheless, we believe this approach to be a more adequate implementation of "irisonly" classification than those presented on [2] and [3] Iris, Non-Occlusion Prob. > 70% Non-iris, area equivalent to Non-Occlusion Prob.…”

Section: Localization Of Gender Cuesmentioning

confidence: 99%

“…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%

“…In a similar approach, [2] used SVM classification of Binarized Statistical Image Features (BSIF) to explore gender prediction not only from normalized irises, but also from the periocular area. They show that the worst accuracy was obtained from normalized iris images (65%), while the best performance was using the entire eye image (84%).…”

Section: Related Workmentioning

confidence: 99%

“…There, it is shown that the presence of eye cosmetics and the use of less rigorous cross-validation protocols may have resulted in optimistic estimates of classification accuracy. Furthermore, more recent works [2,14,3,10] suggest that the majority of gender cues is not found in the iris, but in the periocular region that surrounds it. In this sense, our work deepens the understanding of the problem because: a) we use a stricter definition for "iris-only" images; and b) unlike the aforementioned works, we account for the presence of cosmetics, which is known to cause interference in gender classification.…”

Section: Introductionmentioning

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: Vgg Features With Svm Classifiersupporting

confidence: 87%

Section: Localization Of Gender Cuesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

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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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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Sex-classification from Cellphones Periocular Iris Images

2019

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Selfie soft biometrics has great potential for various applications ranging from marketing, security and online banking. However, it faces many challenges since there is limited control in data acquisition conditions. This chapter presents a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach that increases the resolution of low quality periocular iris images cropped from selfie images of subject's faces. This work shows that increasing image resolution (2x and 3x) can improve the sex-classification rate when using a Random Forest classifier. The best sex-classification rate was 90.15% for the right and 87.15% for the left eye. This was achieved when images were upscaled from 150×150 to 450×450 pixels. These results compare well with the state of the art and show that when improving image resolution with the SRCNN the sex-classification rate increases. Additionally, a novel selfie database captured from 150 subjects with an iPhone X was created (available upon request).

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Iris or Periocular? Exploring Sex Prediction from Near Infrared Ocular Images

Cited by 28 publications

References 12 publications

Predicting Gender From Iris Texture May Be Harder Than It Seems

Predicting Gender From Iris Texture May Be Harder Than It Seems

Authentication through gender classification from iris images using support vector machine

Sex-classification from Cellphones Periocular Iris Images

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