Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijaywada 522302, India.
Department of IT, Neil Gogte Institute of Technology, Kachawanisingaram Village, Hyderabad 500039, India.
Sensors (Basel). 2022 Jul 9;22(14):5160. doi: 10.3390/s22145160.
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.
如今，由于移动电话、笔记本电脑和电子小工具等电子产品在日常生活中的大量使用，对基于软生物识别的设备的需求正在迅速增长。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，因为医院中患者、医生和其他工作人员的所有数据（性别、年龄、面部表情和伪造）都通过数字系统进行管理和转发，以减少文书工作。这一概念使患者和医生之间的关系更加友好，并使随时随地获取医疗报告和治疗变得更加容易。在本文中，我们提出了一种新的基于软生物识别的安全生物识别系统方法，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，使用了基于 5 层 U-Net 的架构进行面部检测，以及基于 Alex-Net 的架构进行面部信息的分类，例如年龄、性别、面部表情和面部伪造等。所提出的模型优于其他最先进的方法。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 照片伪造数据库、CASIA、Adience、群体图像数据集（IOG）、扩展 Cohn-Kanade 数据集 CK+和日本女性面部表情（JAFFE）数据集。所提出的模型在伪造方面的准确率为 94.17%，在年龄方面的准确率为 83.26%，在性别方面的准确率为 95.31%，在面部表情方面的准确率为 96.9%。总的来说，所提出的模型中的修改取得了更好的结果，它将在未来很长一段时间内支持基于软生物识别的应用。
