Using Facial Recognition Tools for Health Assessment

Boczar, Daniel; Ávila, Francisco R.; Carter, Rickey E.; Moore, Pamela A.; Giardi, Davide; Guliyeva, Günel; Bruce, Charles J.; McLeod, Christopher J.; Forte, Antonio J.

doi:10.1097/psn.0000000000000410

Cited by 1 publication

(1 citation statement)

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“…It is also less expensive since it does not have constraints to therapy, unlike other biometrics methods. Although the invention has many benefits, we are going to find that it has a few flaws, which engineers across the world are trying to fix [16]. Individuals can only be identified when the lighting circumstances are favorable, such as when their face turns slightly black or when they appear in insufficient light, as this reduces the application's reliability in recognizing and recognizing the person.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing and Securing a Real-Time Embedded Face Recognition System using Raspberry Pi

Alhajim,

Salman FAHAMA,

T.A

2024

J. Al-Qadisiyah Comp. Sci. Math.

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The modern world is full of data of all kinds; however, the vast amount of video and image data available provides the data set needed for facial recognition. Facial recognition is crucial in safety and surveillance systems that analyze visual data and millions of images. Using facial recognition software, a person's identity can be verified through a variety of media images. For facial recognition, there is a variety of algorithms available. The article presents an approach to a face recognition framework using Haar Cascade, a biometric technology in safety and surveillance systems. It investigates combining standard machine learning techniques for face detection and identification with Raspberry Pi face detection, a cost-effective and easy-to-use embedded system. The system detects faces from indirect and direct images, achieving high speed using the latest Raspberry Pi 4 and Python libraries. The work demonstrates a machine-learning-based design method and a complete embedded system. The face detection accuracy is 92%, and the average time is 0.35 compared to the local binary model (LBP). Many facial recognition algorithms on the web and in literature reviews are vulnerable to image attacks. These methods are very effective in identifying faces in webcams, video streams, images, and videos. This system's use of the Raspberry Pi 4 and advanced Python libraries results in fast and accurate real-time face detection. This paper extends the work first presented at the 12th Iranian/Second International Conference on Machine Vision and Image Processing (MVIP).

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Section: Literature Reviewmentioning

confidence: 99%