DESIGN OF FACE RECOGNITION ANTI-SPOOFING MODEL BASED ON CONVOLUTIONAL NEURAL NETWORK USING FACENET TO ADDRESS VIDEO REPLAY ATTACKS
Face-based biometric systems had received increasing attention because they had the richest and most easily accessible information in everyday life. However, the video replay attack had been a more challenging face presentation attack than other face presentation attacks because it could exhibit...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/78321 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Face-based biometric systems had received increasing attention because they had
the richest and most easily accessible information in everyday life. However, the
video replay attack had been a more challenging face presentation attack than
other face presentation attacks because it could exhibit facial liveliness signals and
was able to bypass face spoofing detectors. This research was developed using
Design Research Methodology to design a Face Recognition Anti-Spoofing model
to address video replay attacks. A FaceNet face recognition model with a Face
Anti-Spoofing Convolutional Neural Network-based method was proposed and
developed using feature matching techniques in an effort to improve authentication
security. This research was conducted through field testing on 33 participants using
their respective facial identities to measure the accuracy of the proposed model in
the login scenario test and evaluated using System Engineering Principles and
Practice. The test results using the feature matching technique showed that the
accuracy of the model reached 97.57% and the F-score value was 96.22%.
Therefore, the proposed model had proven to be effective in addressing video replay
attacks through attack scenario testing. |
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