ml-driven wearables biometrics

On Finger Stretching and Bending Dynamics as a Biometric Modality

Studies on the characterization of the dexterity of fingers and hands improve the understanding of how humans interact with computing devices. In this study, finger bending patterns captured by flex sensors worn on the fingers are characterized to build a biometric authentication system. The modality uses an array of resistive sensors fitted in a smart glove worn by users while typing. The study encompasses 55 users, 23 of them entered a 9-digit PIN on a laptop’s number pad, and 32 of them typed a 10-length alphanumeric password on the full-sized keyboard. The results demonstrate that the users are authenticated using features built from the flex sensors relating to their PIN and password with a mean EER score of 7.49% and 9.76%, respectively. We further assessed the potential of using individual fingers to authenticate users in both the biometric systems and found that even the fingers not used for typing ex- hibited discriminative patterns due to movement dynamics during the typing process. This assessment highlights the potential for designing lightweight biometric modalities utilizing dexterity and patterns based on fewer fingers.

The contributions of the paper are summarized below:

1) Introducing finger bending patterns as a biometric authentication modality:

We study the use of finger bending patterns as captured by flex sensors embedded in hand gloves as a biometric authentication modality. Based on experiments in which people entered PINs (i.e., numeric inputs) and passwords (i.e., alphanumeric inputs) onto a laptop keyboard, we show the modality to attain Equal Error Rates (EERs) of between 7.49% and 9.76%. While these error rates might not be low enough for a stand-alone authentication modality, they are com- petitive with the state-of-the-art error rates seen with other behavioral biometric modalities (e.g., keystroke dynamics, gait, etc.). And it provides evidence for finger bending patterns as having potential applications in multi-modal systems that leverage multiple sources of information for strong authentication. Such applications could be in the field of gaming or in medical settings where gloves are inherently part of the gear worn during interactions with technology.

2) Sensitivity analysis of the performance of the finger bending biometric modality:

To more deeply understand the dynamics of this biometric modality, we study how different key variables affect its performance. For example, we study the contribution of individual fingers to the overall system performance, providing an interesting perspective of which fingers more uniquely identify users during typing. We also study the use of one glove (such as in PIN entry on the number pad) vs. two gloves (such as in password entry on the entire keyboard), two alternate configurations that practitioners using this system could choose from in practice. Finally, to understand how different users contribute to the con- solidated system error rate, we also examine and discuss the EER score distributions across the population.