Estimation of Face Attributes Using Standard CNN Features

Shah, Jaymeel; Pandey, Arvind Kumar

doi:10.1109/icacite57410.2023.10183194

Cited by 1 publication

(1 citation statement)

References 13 publications

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“…Figure 1 serves as an exemplar of the dataset employed for the implementation database. In this study, the decision was made to compare two different detection methods: utilizing YOLO (You Only Look Once) [18], [19] and a combined method of the Haar Cascade Algorithm with Convolutional Neural Network (CNN) [20]. YOLO is a real-time object detection algorithm that divides the image into a grid and predicts bounding boxes and object classes in a single processing step [21].…”

Section: Methodsmentioning

confidence: 99%

YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition

Dewanto,

Farid,

Rafdi Syah

et al. 2024

SJI

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Purpose: This research investigates the effectiveness of YOLO (You Only Look Once) and Convolutional Neural Network (CNN) in real-time face mask recognition, addressing the challenges posed by mask-wearing in infectious disease prevention.Method: Utilizing a diverse dataset and employing YOLO's object detection and a combined Haar Cascade Algorithm with CNN, the study evaluated key performance indicators, including accuracy, framerate, and F1 Score.Results: Results indicated that CNN outperformed YOLO in accuracy (99.3% vs. 79.3%) but operated at a slightly lower framerate. YOLO excelled in recall and precision, presenting a compelling choice for specific application needs. The research underscores the importance of considering factors beyond accuracy for informed decision-making in the realm of face mask recognition.Novelty: This research evaluates the real-time performance of YOLO and CNN algorithms in masked face recognition, highlighting the crucial balance between framerate efficiency and detection accuracy.

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