Model of the "biometry-code" converter based on artificial neural networks for analysis of facial thermograms

P. Lozhnikov, S. Zhumazhanova
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Abstract

Existing asymmetric encryption algorithms involve the storage of a secret private key, authorized access to which, as a rule, is carried out upon presentation of a password. Passwords are vulnerable to social engineering and human factors. Combining biometric security techniques with cryptography is seen as a possible solution to this problem, but any biometric cryptosystem should be able to overcome the small differences that exist between two different implementations of the same biometric parameter. This is especially true for dynamic biometrics, when differences can be caused by a change in the psychophysiological state of the subject. The solution to the problems is the use of a system based on the "biometrics-code" converter, which is configured to issue a user key after presentation of his/her biometric image. In this case, the key is generated in advance in accordance with accepted standards without the use of biometric images. The work presents results on using thermal images of a user for reliable biometric authentication based on a neural network "biometrics-code" converter. Thermal images have recently been used as a new approach in biometric identification systems and are a special type of biometric images that allow us to solve the problem of both the authentication of the subject and the identification of his psychophysiological state. The advantages of thermal imaging are that this technology is now becoming available and mobile, allowing the user to be identified and authenticated in a non-contact and continuous manner. In this paper, an experiment was conducted to verify the images of thermograms of 84 subjects and the following indicators of erroneous decisions were obtained: EER = 0.85 % for users in the "normal"state.
基于人工神经网络的人脸热图分析“生物识别码”转换器模型
现有的非对称加密算法涉及秘密私钥的存储,通常在提供密码后进行授权访问。密码容易受到社会工程和人为因素的影响。将生物识别安全技术与密码学相结合被视为解决该问题的可能方案,但是任何生物识别密码系统都应该能够克服相同生物识别参数的两种不同实现之间存在的微小差异。对于动态生物识别技术来说尤其如此,当差异可能由受试者的心理生理状态的变化引起时。解决这些问题的方法是使用基于“生物识别代码”转换器的系统,该系统被配置为在呈现他/她的生物识别图像后发出用户密钥。在这种情况下,密钥是按照公认的标准提前生成的,而不使用生物识别图像。这项工作展示了基于神经网络“生物识别代码”转换器使用用户的热图像进行可靠的生物识别认证的结果。热图像是近年来应用于生物识别系统的一种新方法,它是一种特殊类型的生物识别图像,可以解决受试者的身份验证和其心理生理状态的识别问题。热成像的优势在于,这项技术现在变得可用和移动,允许用户以非接触和连续的方式进行身份识别和认证。本文通过实验对84名被试的热像图图像进行了验证,得到了以下错误决策指标:“正常”状态下的用户EER = 0.85%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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