Health Monitoring System Based on Foot Print using High End Processor
M. Reji1, A. Mohammed Irfan2

1M. Reji*, Associate Professor, Department of ECE, Saveetha School of Engineering, SIMATS, , Chennai, India.
2A. Mohammed Irfan, UG Scholar, Department of ECE, Saveetha School of Engineering, SIMATS, Chennai, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 27, 2020. | Manuscript published on March 10, 2020. | PP: 2250-2252 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2088039520/2020©BEIESP | DOI: 10.35940/ijitee.E2088.039520
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Abstract: Biometrics is used for identification in this article. This paper also describes how biometrics can leverage the boundless computational resources of the cloud and the striking properties of flexibility, scalability, and cost reduction to reduce the cost of the biometric system requirements of different computational resources (i.e. processing power or data storage) and to improve the performance of the processes of the biometric systems (i.e. biometric matching). The human footprint is known to have the new characteristics that could be used to identify the criteria for determining an individual’s identity. The main objective is to develop image processing algorithms capability on a limited computing platform. We created the embedded framework that recognizes and accepts a person’s identity. The paper’s main purpose is to update the characteristics of the details, needs, and reports of the patient behind the implementation of a real-time base system. The foot picture of the human is segmented, and its key points are placed. The foot is arranged and trimmed, clipped according to key points, created and dimensioned. Colour establishes a crucial role in numerous footprint recognition applications. Due to the drawback of real time software and Raspberry Pi technology, this effort based on lightweight methodology was primarily used. 
Keywords: Raspberry pi, an Automated Biometric Feature From the Footprint, Personal Identification.
Scope of the Article: Health Monitoring and Life Prediction of Structures