Multiplication-Free Biometric Recognition for Faster Processing under Encryption

Amina Bassit*, Florian Hahn, Raymond N.J. Veldhuis, Andreas Peter

*Corresponding author for this work

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The cutting-edge biometric recognition systems extract distinctive feature vectors of biometric samples using deep neural networks to measure the amount of (dis-)similarity between two biometric samples. Studies have shown that personal information (e.g., health condition, ethnicity, etc.) can be inferred, and biometric samples can be reconstructed from those feature vectors, making their protection an urgent necessity. State-of-the-art biometrics protection solutions are based on homomorphic encryption (HE) to perform recognition over encrypted feature vectors, hiding the features and their processing while releasing the outcome only. However, this comes at the cost of those solutions' efficiency due to the inefficiency of HE-based solutions with a large number of multiplications; for (dis-)similarity measures, this number is proportional to the vector's dimension. In this paper, we tackle the HE performance bottleneck by freeing the two common (dis-)similarity measures, the cosine similarity and the squared Euclidean distance, from multiplications. Assuming normalized feature vectors, our approach pre-computes and organizes those (dis-)similarity measures into lookup tables. This transforms their computation into simple table-lookups and summation only. We study quantization parameters for the values in the lookup tables and evaluate performances on both synthetic and facial feature vectors for which we achieve a recognition performance identical to the non-tabularized baseline systems. We then assess their efficiency under HE and record runtimes between 28.95ms and 59.35ms for the three security levels, demonstrating their enhanced speed.
Original languageEnglish
Title of host publication2022 IEEE International Joint Conference on Biometrics (IJCB)
Place of PublicationPiscataway, NJ
Number of pages9
ISBN (Electronic)978-1-6654-6394-2
ISBN (Print)978-1-6654-6395-9
Publication statusPublished - 17 Jan 2023
EventIEEE International Joint Conference on Biometrics, IJCB 2022 - Abu Dhabi, United Arab Emirates
Duration: 10 Oct 202213 Oct 2022

Publication series

NameIEEE International Joint Conference on Biometrics (IJCB)
ISSN (Print)2474-9680
ISSN (Electronic)2474-9699


ConferenceIEEE International Joint Conference on Biometrics, IJCB 2022
Abbreviated titleIJCB
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi


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