EYE SPY Face Detection and Identification using YOLO

Vigil, M. S. Antony; Barhanpurkar, Manasi Makarand; Anand, NS Rahul; Soni, Yash; Anand, Anmol

doi:10.1109/icssit46314.2019.8987830

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2019 International Conference on Smart Systems and Inventive Technology (ICSSIT) 2019

DOI: 10.1109/icssit46314.2019.8987830

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EYE SPY Face Detection and Identification using YOLO

M. S. Antony Vigil

Manasi Makarand Barhanpurkar

NS Rahul Anand

et al.

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Cited by 2 publications

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“…The proposed framework's accuracy, recall, F-measure, and IOU were 0.965, 0.963, 0.97, and 0.972. Vigil et al [31] apply YOLO, R-CNN, WordTree, ImageNet, and COCO. There are 9418 classes in the WordTree datasets.…”

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confidence: 99%

Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach

Bitto

Mahmud

2022

Bulletin EEI

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Among the most important systems in the body is the eyes. Although their small stature, humans are unable to imagine existence without it. The human optic is safe against dust particles by a narrow layer called the conjunctiva. It prevents friction during the opening and shutting of the eye by acting as a lubricant. A cataract is an opacification of the eye's lens. There are various forms of eye problems. Because the visual system is the most important of the four sensory organs, external eye abnormalities must be detected early. The classification technique can be used in a variety of situations. A few of these uses are in the healthcare profession. We use visual geometry group (VGG-16), ResNet-50, and Inception-v3 architectures of convolutional neural networks (CNNs) to distinguish between normal eyes, conjunctivitis eyes, and cataract eyes throughout this paper. With a detection time of 485 seconds, Inception-v3 is the most accurate at detecting eye disease, with a 97.08% accuracy, ResNet-50 performs the second-highest accuracy with 95.68% with 1090 seconds and lastly, VGG-16 performs 95.48% accuracy taking the highest time of 2510 seconds to detect eye diseases.

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confidence: 99%

Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach

Bitto

Mahmud

2022

Bulletin EEI

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Artificial intelligence inspired IoT-fog based framework for generating early alerts while train passengers traveling in dangerous states using surveillance videos

Saini

Sengupta

Singh

2023

Multimed Tools Appl

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EYE SPY Face Detection and Identification using YOLO

Cited by 2 publications

References 6 publications

Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach

Multi categorical of common eye disease detect using convolutional neural network: a transfer learning approach

Artificial intelligence inspired IoT-fog based framework for generating early alerts while train passengers traveling in dangerous states using surveillance videos

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