Research on hybrid transformer-based autoencoders for user biometric verification
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Дата
2023
Автори
Науковий керівник
Назва журналу
Номер ISSN
Назва тому
Видавець
КПІ ім. Ігоря Сікорського
Анотація
Abstract. Our current study extends previous work on motion-based biometric verification using sensory data by exploring new architectures and more complex input from various sensors. Biometric verification offers advantages like uniqueness and protection against fraud. The state-of-the-art transformer architecture in AI is known for its attention block and applications in various fields, including NLP and CV. We investigated its potential value for applications involving sensory data. The research proposes a hybrid architecture, integrating transformer attention blocks with different autoencoders, to evaluate its efficacy for biometric verification and user authentication. Various configurations were compared, including LSTM autoencoder, transformer autoencoder, LSTM VAE, and transformer VAE. Results showed that combining transformer blocks with an undercomplete deterministic autoencoder yields the best performance, but model performance is significantly influenced by data preprocessing and configuration parameters. The application of transformers for biometric verification and sensory data appears promising, performing on par with or surpassing LSTM-based models but with lower inference and training time.
Опис
Ключові слова
biometric verification, transformers, variational autoencoder, transformer autoencoder, біометрична верифікація, транформери, варіаційний автокодувальник, автокодувальник на основі трансфомера
Бібліографічний опис
Havrylovych, M. P. Research on hybrid transformer-based autoencoders for user biometric verification / M. P. Havrylovych, V. Y. Danylov // Системні дослідження та інформаційні технології : міжнародний науково-технічний журнал. – 2023. – № 3. – С. 42-53. – Бібліогр.: 27 назв.