Authentication through gender classification from iris images using support vector machine

Abstract

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.

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

“…In 2021, Khan et al (2021) , proposed an authentication technique based on the SVM classification of iris images. It has a great reaction to persistent changes when the Zernike, Legendre, and Gradient-oriented histograms are used.…”

“…Ø The solution lies in developing a new classifier that utilizes discriminative characteristics while maintaining the same capabilities as full-connection layers or SoftMax classifiers (Alghaili et al, 2020). According to A. R. Khan (A. R. Khan et al, 2021) , a hybrid classifier combining CNN and Support Vector Machine (SVM) yielded much better results than just a CNN. Since SVMs are so complex, it is critically important to identify alternative classifiers that require fewer tuning parameters, perform well in classification, and are capable of handling the same tasks.…”

Microscopic retinal blood vessels detection and segmentation using support vector machine and K‐nearest neighbors

“…Retinal vessel segmentation requires high abilities, which makes high solicitation for fast analyses of retinal vessels. Every individual has his unique retinal vessels' distribution, as all biometrics as iris face or fingerprint (Khan, Doosti, et al, 2021; Meethongjan et al, 2013; Saba, 2020). The example of the retina's vessel is distinctive in any event, for twins (Rashid et al, 2020).…”

“…The example of the retina's vessel is distinctive in any event, for twins (Rashid et al, 2020). Although this pattern may change because of certain diseases (Khan, Doosti, et al, 2021; Khan, Khan, et al, 2021; Yousuf et al, 2018), for example, glaucoma, diabetes, and lack of immune system, it is fixed during human life (Rahim, Norouzi, et al, 2017).…”

Finger Vein Authentication Based on Wavelet Scattering Networks

“…The user may place the finger at an incorrect angle or location when placing the finger on the scanner for identification and the test image may not be the same as the training image. It is also possible to apply noise to the scanner, reducing the identification rate like the challenges mentioned above [18][19][20]. This paper attempts to introduce a method based on the response to the challenge.…”