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- W4386901419 abstract "Face recognition is the science of identifying and recognizing human faces in various situations, keeping the constraints like pose variation and occlusion in mind. Due to its impactful applications in safety and security systems, face recognition is becoming extremely popular and is researched extensively even today. The FaceNet method is among the most tested approaches in Deep learning face identification. This method uses a deep convolution neural network for training the data. The face embedding generated can be used to train a face identification system. This study aims to comprehend the FaceNet system, evaluate its performance, and test its accuracy on seven standard datasets. The study also tries to compare how well the FaceNet method works compared to other popular holistic and hybrid methods. From the outcomes of this study, we can conclude that FaceNet showed outstanding results and was better than the other methods. The FaceNet system reached a minimum of 90% accuracy on all standard datasets used on both the pre-trained models, which is a significant number for any face recognition method." @default.
- W4386901419 created "2023-09-21" @default.
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- W4386901419 date "2023-01-01" @default.
- W4386901419 modified "2023-09-27" @default.
- W4386901419 title "A Quantitative Study on the FaceNet System" @default.
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- W4386901419 doi "https://doi.org/10.1007/978-981-99-4284-8_17" @default.
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