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 exhibited 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.