TY - GEN
T1 - A Quantitative Study on the FaceNet System
AU - Gopakumar, Rajesh
AU - Kotegar, Karunakar A.
AU - Vishal Anand, M.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
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U2 - 10.1007/978-981-99-4284-8_17
DO - 10.1007/978-981-99-4284-8_17
M3 - Conference contribution
AN - SCOPUS:85174509082
SN - 9789819942831
T3 - Lecture Notes in Networks and Systems
SP - 211
EP - 223
BT - Advanced Computational and Communication Paradigms - Proceedings of ICACCP 2023
A2 - Borah, Samarjeet
A2 - Gandhi, Tapan K.
A2 - Piuri, Vincenzo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023
Y2 - 16 February 2023 through 18 February 2023
ER -