A comparative analysis of multi-biometrics performance in human and action recognition using silhouette thermal-face and skeletal data

• Published in: Neural networks Vol. 170; pp. 1 - 17
• Main Authors: Kurban, Onur Can, Yildirim, Tülay
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-02-2024
• Subjects: Artificial Intelligence; Behavioural biometrics; Biometric Identification - methods; Biometry - methods; Databases, Factual; Deep learning; Energy images; Humans; Thermal face; Deep learning; Energy images; Thermal face; Behavioural biometrics
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2023.10.016

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.