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Face Detection and Recognition for Patient

Kamlesh Gautam, Manish Dubey, Nikita Jain

Abstract


Biometric authentication demands novel and effective protection techniques to distinguish a real trait from an independently produced synthetic or rebuilt sample. This work presents a software-based false detection approach that may be used in a variety of biometric systems to identify numerous types of unauthorized entry attempts. The suggested method uses picture quality assessment to provide liveness assessment to biometric recognition frameworks in a rapid, easy-to-use, and unobtrusive fashion to increase security. For real-time applications, the proposed solution is simple. It differentiates between authentic and counterfeit samples based on 25 generic image quality characteristics extracted from a single image (the same image used for authentication). The experimental results, obtained on publicly available fingerprint, iris, and 2D face data sets, demonstrate that the proposed method is highly competitive in comparison to other state-of-the-art approaches and that the general image quality of real biometric samples reveals highly valuable information that can be used to distinguish them from fake traits. Therefore, this work is helpful in the medical field regarding patients.

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References


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