Biometric recognition is currently implemented in several authentication contexts, most recently in mobile devices where it is expected to complement or even replace traditional authentication modalities such as PIN (Personal Identification Number) or passwords. The assumed convenience characteristics of biometrics are transparency, reliability and ease-of-use, however, the question of whether biometric recognition is as intuitive and straightforward to use is open to debate. Can biometric systems make some tasks easier for people with accessibility concerns? To investigate this question, an accessibility evaluation of a mobile app was conducted where test subjects withdraw money from a fictitious ATM (Automated Teller Machine) scenario. The biometric authentication mechanisms used include face, voice, and fingerprint. Furthermore, we employed traditional modalities of PIN and pattern in order to check if biometric recognition is indeed a real improvement. The trial test subjects within this work were people with real-life accessibility concerns. A group of people without accessibility concerns also participated, providing a baseline performance. Experimental results are presented concerning performance, HCI (Human-Computer Interaction) and accessibility, grouped according to category of accessibility concern. Our results reveal links between individual modalities and user category establishing guidelines for future accessible biometric products.
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Sensors (Basel)
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Department of Biomedical Engineering, Lebanese International University, Beirut P.O. Box 146404, Lebanon.
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Department of Neurosurgery, University Medicine Greifswald, Greifswald, Germany.
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PLoS One
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College of Artificial Intelligence Technology, Changchun Institute of Technology, Changchun, China.
The application of face recognition technology in Library Access Control System (LACS) has an important impact on improving the security and management efficiency of the library. However, the traditional face recognition methods have some limitations in the face of complex environmental conditions such as illumination and posture change. To solve this problem, an improved method combining the Aggregating Spatial Embeddings for Face Recognition (ASEF) algorithm and Principal Component Analysis (PCA) is proposed.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.
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View Article and Find Full Text PDFArch Pharm (Weinheim)
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