Face Spoofing Detection using Inception-v3 on RGB Modal and Depth Modal
Dublin Core
Title
Face Spoofing Detection using Inception-v3 on RGB Modal and Depth Modal
Subject
face spoofing, real, spoof, Inception-v3, depth
Description
Face spoofing can provide inaccurate face verification results in the face recognition
system. Deep learning has been widely used to solve face spoofing problems. In face
spoofing detection, it is unnecessary to use the entire network layer to represent the
difference between real and spoof features. This study detects face spoofing by cutting the
Inception-v3 network and utilizing RGB modal, depth, and fusion approaches. The results
showed that face spoofing detection has a good performance on the RGB and fusion
models. Both models have better performance than the depth model because RGB modal
can represent the difference between real and spoof features, and RGB modal dominate the
fusion model. The RGB model has accuracy, precision, recall, F1-score, and AUC values
obtained respectively 98.78%, 99.22%, 99.31.2%, 99.27%, and 0.9997 while the fusion
model is 98.5%, 99.31%, 98.88%. 99.09%, and 0.9995, respectively. Our proposed method
with cutting the Inception-v3 network to mixed6 successfully outperforms the previous study with accuracy up to 100% using the MSU MFSD benchmark dataset.
system. Deep learning has been widely used to solve face spoofing problems. In face
spoofing detection, it is unnecessary to use the entire network layer to represent the
difference between real and spoof features. This study detects face spoofing by cutting the
Inception-v3 network and utilizing RGB modal, depth, and fusion approaches. The results
showed that face spoofing detection has a good performance on the RGB and fusion
models. Both models have better performance than the depth model because RGB modal
can represent the difference between real and spoof features, and RGB modal dominate the
fusion model. The RGB model has accuracy, precision, recall, F1-score, and AUC values
obtained respectively 98.78%, 99.22%, 99.31.2%, 99.27%, and 0.9997 while the fusion
model is 98.5%, 99.31%, 98.88%. 99.09%, and 0.9995, respectively. Our proposed method
with cutting the Inception-v3 network to mixed6 successfully outperforms the previous study with accuracy up to 100% using the MSU MFSD benchmark dataset.
Creator
Yuni Arti, Aniati Murni Arymurth
Source
http://dx.doi.org/10.21609/jiki.v16i1.1100
Publisher
Faculty of Computer Science Universitas Indonesia
Date
2023-02-28
Contributor
Sri Wahyuni
Rights
e-ISSN : 2502-9274 printed ISSN : 2088-7051
Format
PDF
Language
English
Type
Text
Coverage
Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Files
Collection
Citation
Yuni Arti, Aniati Murni Arymurth, “Face Spoofing Detection using Inception-v3 on RGB Modal and Depth Modal,” Repository Horizon University Indonesia, accessed June 12, 2025, https://repository.horizon.ac.id/items/show/8851.