如今,由于手机等电子产品的大量使用,对基于生物特征的软设备的需求正在迅速增加,日常生活中的笔记本电脑和电子产品。最近,医疗保健部门也出现了软生物识别技术,即,面部生物识别技术,因为整个数据,即,(性别,年龄,患者的面部表情和欺骗),医院的医生和其他工作人员通过数字系统管理和转发,以减少文书工作。这个概念使病人和医生之间的关系更友好,使获得医疗报告和治疗更容易,生活中的任何地方和任何时刻。在本文中,我们提出了一种新的基于软生物识别的方法,用于安全的生物识别系统,因为医疗信息在我们的生活中起着至关重要的作用。在提出的模型中,5层基于U-Net的架构用于面部检测,基于Alex-Net的架构用于面部信息的分类,即年龄,性别,面部表情和面部欺骗,等。所提出的模型优于其他现有技术方法。所提出的方法在六个基准数据集上进行了评估和验证,即,NUAA照片冒名顶替者数据库,卡西亚,Adience,组数据集(IOG)的图像,扩展Cohn-Kanade数据集CK+和日本女性面部表情(JAFFE)数据集。所提出的模型实现了94.17%的欺骗精度,83.26%的年龄,性别占95.31%,面部表情占96.9%。总的来说,在提出的模型中进行的修改给出了更好的结果,它将在未来很长的路要走,以支持基于软生物特征的应用。
Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.