2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-MMSDF-2.7
Paper Title PERSONALIZED FACE AUTHENTICATION BASED ON FEW-SHOT META-LEARNING
Authors Chaehun Shin, Jangho Lee, Byunggook Na, Sungroh Yoon, Seoul National University, Republic of Korea
SessionSS-MMSDF-2: Special Session: AI for Multimedia Security and Deepfake 2
LocationArea A
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for information forensics and security
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Abstract Existing face authentication methods rely heavily on the general feature extractor trained with face verification algorithms without considering the target identity. No consideration of the target identity causes inefficiency in that the face authentication works on a specific target identity. Personalized face authentication systems that integrate target identity information is demanded to yield an efficient and superior performance than current methods. With the help of fast adaptation in few-shot meta-learning, we propose a novel personalized Face Authentication based on few-shot Meta-Learning (FAML). FAML introduces a unique person-specific binary classification task and trains an initialization model that can evolve into the personalized face authentication model for any target identity with few data and a small number of iterations. We evaluate the FAML on two public datasets, and it outperforms the baselines by a large margin and demonstrates the practicability as a security application.