As biometric traits, face, voice, fingerprint, hand veins, iris, handwriting, and gait are available for such applications.
In the recent years, biometric features such as face, iris, fingerprint, hand geometry, palmprint, and signature have been suggested for the security in access control.
Originally, the desktop scenario dataset contained voice, face, signature, fingerprint, hand, and iris modalities, and data were acquired in a desktop-based office environment.
Biometrics authentication refers to establishing identity based on the physical and/or behavioral characteristics of a person such as face, fingerprint, hand geometry, iris, voice, way of walking, and so forth.
The physiological information extracted from human body including the features of individual face, palm-print or fingerprint, hand-shape, skin, temperature and arterial pulse, etc. is used to recognize personal identification and diagnose some diseases.
Physiological biometric traits, such as fingerprints, hand geometry, retina, iris, and facial images, are collected from direct measurements of the human body, while behavioural biometric characteristics, such as signature, keystroke rhythms, gait pattern, and voice recordings, are associated with a specific set of actions of a person.
Biometric systems establish the identity of an individual based on physical characteristics, such as fingerprint, face, hand, and iris, or behavioral characteristics, such as gait, signature, handwriting, and keystroke dynamics.
In this work, three different design frameworks for multimodal biometric systems based on fingerprint and hand geometry modalities are proposed.
It examined seven technologies that might be used for border control, including facial recognition, fingerprint recognition, hand geometry, iris recognition, retina recognition, signature recognition and speaker recognition.
Face, fingerprint, and hand veins databases.
For the fingerprint and hand veins databases, we learn two multi-class SVM models, respectively.
