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.

中文翻译：

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使用支持向量机通过虹膜图像的性别分类进行身份验证

软生物识别信息，例如性别、虹膜和语音，可用于各种应用，例如安全、身份验证和验证。虹膜是安全的生物识别技术，由于其高度确定的功能在过去几十年中得到了使用，因此具有低伪造和低错误率。虹膜识别既可以独立使用，也可以部分用于安全识别和认证系统。现有的基于虹膜的性别分类技术准确率低，计算复杂度高。因此，本文提出了一种通过使用支持向量机 (SVM) 对虹膜图像进行性别分类的身份验证方法，该方法使用 Zernike、Legendre 不变矩和面向梯度的直方图对持续变化具有出色的响应。在这项研究中，不变矩被用作虹膜图像的特征提取。提取这些描述符的属性后，通过键码融合对属性进行分类。SVM 用于使用融合特征向量进行性别分类。所提出的方法在 CVBL 数据集上进行评估，并基于局部二进制模式和 Gabor 滤波器在现有技术中比较结果。所提出的方法具有 98% 的性别分类率，计算复杂度低，可用作身份验证措施。所提出的方法在 CVBL 数据集上进行评估，并基于局部二进制模式和 Gabor 滤波器在现有技术中比较结果。所提出的方法具有 98% 的性别分类率，计算复杂度低，可用作身份验证措施。所提出的方法在 CVBL 数据集上进行评估，并基于局部二进制模式和 Gabor 滤波器在现有技术中比较结果。所提出的方法具有 98% 的性别分类率，计算复杂度低，可用作身份验证措施。