TY - GEN
T1 - Disguise Face Classification Using EfficientNet Deep Learning
AU - Padmashree, G.
AU - Wagle, Shruti G.
AU - Karunakar, A. K.
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - With the rise in popularity of social media and intelligent gadgets, one essential biometrics for identifying people is their face. The efficiency of existing automatic face recognition systems has decreased due to factors such as face ageing, conceals, and pose variations. Face recognition algorithms must be more accurate for recognizing faces hidden behind masks and makeup, as security and surveillance requirements become more stringent. Disguise face classification acts as a standalone early warning system in such scenarios. The major goal of this research was to see how the EfficientNet family of models compares to the current state-of-the-art architecture in terms of disguise face classification. For disguise face classification, the EfficientNet deep learning architecture was proposed in this paper. The models were trained and tested using the Disguise Faces in Wild (DFW) 2018 data set. The transfer learning method was used to train EfficientNet and other deep learning models. Results obtained proved that EfficientNet-B3 has outperformed other EfficientNet architectures with 92.2% precision and 93.9% accuracy.
AB - With the rise in popularity of social media and intelligent gadgets, one essential biometrics for identifying people is their face. The efficiency of existing automatic face recognition systems has decreased due to factors such as face ageing, conceals, and pose variations. Face recognition algorithms must be more accurate for recognizing faces hidden behind masks and makeup, as security and surveillance requirements become more stringent. Disguise face classification acts as a standalone early warning system in such scenarios. The major goal of this research was to see how the EfficientNet family of models compares to the current state-of-the-art architecture in terms of disguise face classification. For disguise face classification, the EfficientNet deep learning architecture was proposed in this paper. The models were trained and tested using the Disguise Faces in Wild (DFW) 2018 data set. The transfer learning method was used to train EfficientNet and other deep learning models. Results obtained proved that EfficientNet-B3 has outperformed other EfficientNet architectures with 92.2% precision and 93.9% accuracy.
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U2 - 10.1007/978-981-19-5403-0_26
DO - 10.1007/978-981-19-5403-0_26
M3 - Conference contribution
AN - SCOPUS:85144208309
SN - 9789811954023
T3 - Smart Innovation, Systems and Technologies
SP - 305
EP - 314
BT - Human-Centric Smart Computing - Proceedings of ICHCSC 2022
A2 - Bhattacharyya, Siddhartha
A2 - Banerjee, Jyoti Sekhar
A2 - Köppen, Mario
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Conference on Human-Centric Smart Computing, ICHCSC 2022
Y2 - 27 April 2022 through 29 April 2022
ER -