TY - JOUR
T1 - Hand grasping synergies as biometrics
AU - Patel, Vrajeshri
AU - Thukral, Poojita
AU - Burns, Martin K.
AU - Florescu, Ionut
AU - Chandramouli, Rajarathnam
AU - Vinjamuri, Ramana
N1 - Publisher Copyright:
© 2017 Patel, Thukral, Burns, Florescu, Chandramouli and Vinjamuri.
PY - 2017/5/2
Y1 - 2017/5/2
N2 - Recently, the need for more secure identity verification systems has driven researchers to explore other sources of biometrics. This includes iris patterns, palm print, hand geometry, facial recognition, and movement patterns (hand motion, gait, and eye movements). Identity verification systems may benefit from the complexity of human movement that integrates multiple levels of control (neural, muscular, and kinematic). Using principal component analysis, we extracted spatiotemporal hand synergies (movement synergies) from an object grasping dataset to explore their use as a potential biometric. These movement synergies are in the form of joint angular velocity profiles of 10 joints. We explored the effect of joint type, digit, number of objects, and grasp type. In its best configuration, movement synergies achieved an equal error rate of 8.19%. While movement synergies can be integrated into an identity verification system with motion capture ability, we also explored a camera-ready version of hand synergies-postural synergies. In this proof of concept system, postural synergies performed well, but only when specific postures were chosen. Based on these results, hand synergies show promise as a potential biometric that can be combined with other hand-based biometrics for improved security.
AB - Recently, the need for more secure identity verification systems has driven researchers to explore other sources of biometrics. This includes iris patterns, palm print, hand geometry, facial recognition, and movement patterns (hand motion, gait, and eye movements). Identity verification systems may benefit from the complexity of human movement that integrates multiple levels of control (neural, muscular, and kinematic). Using principal component analysis, we extracted spatiotemporal hand synergies (movement synergies) from an object grasping dataset to explore their use as a potential biometric. These movement synergies are in the form of joint angular velocity profiles of 10 joints. We explored the effect of joint type, digit, number of objects, and grasp type. In its best configuration, movement synergies achieved an equal error rate of 8.19%. While movement synergies can be integrated into an identity verification system with motion capture ability, we also explored a camera-ready version of hand synergies-postural synergies. In this proof of concept system, postural synergies performed well, but only when specific postures were chosen. Based on these results, hand synergies show promise as a potential biometric that can be combined with other hand-based biometrics for improved security.
KW - Biometrics
KW - Grasping
KW - Human hand
KW - Principal component analysis
KW - Synergies
UR - http://www.scopus.com/inward/record.url?scp=85029607004&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029607004&partnerID=8YFLogxK
U2 - 10.3389/fbioe.2017.00026
DO - 10.3389/fbioe.2017.00026
M3 - Article
AN - SCOPUS:85029607004
VL - 5
JO - Frontiers in Bioengineering and Biotechnology
JF - Frontiers in Bioengineering and Biotechnology
IS - MAY
M1 - 26
ER -