Department of Electronics and Communications Engineering, Yildiz Technical University, 34220, Istanbul, Turkey.
Neural Netw. 2024 Feb;170:1-17. doi: 10.1016/j.neunet.2023.10.016. Epub 2023 Nov 8.
Biometrics is a field that has been given importance in recent years and has been extensively studied. Biometrics can use physical and behavioural differences that are unique to individuals to recognize and identify them. Today, biometric information is used in many areas such as computer vision systems, entrance systems, security and recognition. In this study, a new biometrics database containing silhouette, thermal face and skeletal data based on the distance between the joints was created to be used in behavioural and physical biometrics studies. The fact that many cameras were used in previous studies increases both the processing intensity and the material cost. This study aimed to both increase the recognition performance and reduce material costs by adding thermal face data in addition to soft and behavioural biometrics with the optimum camera. The presented data set was created in accordance with both motion recognition and person identification. Various data loss scenarios and multi-biometrics approaches based on data fusion have been tried on the created data sets and the results have been given comparatively. In addition, the correlation coefficient of the motion frames method to obtain energy images from silhouette data was tested on this dataset and yielded high-accuracy results for both motion and person recognition.
生物识别技术是近年来受到重视并得到广泛研究的一个领域。生物识别技术可以利用个体独有的生理和行为差异来识别和验证他们的身份。如今，生物识别信息被广泛应用于计算机视觉系统、门禁系统、安全和识别等领域。在这项研究中，创建了一个新的生物识别数据库，该数据库包含基于关节间距离的轮廓、热脸和骨骼数据，用于行为和物理生物识别研究。由于之前的研究中使用了许多摄像机，因此处理强度和材料成本都增加了。本研究旨在通过添加热脸数据，以及优化的摄像机，对软生物识别和行为生物识别进行补充，从而提高识别性能并降低材料成本。所提出的数据集是根据运动识别和人员识别创建的。在创建的数据集上尝试了各种数据丢失情况和基于数据融合的多生物识别方法，并进行了比较。此外，还在该数据集上测试了从轮廓数据获取能量图像的运动帧方法的相关系数，该方法在运动和人员识别方面都取得了高精度的结果。
