TY - CHAP
T1 - User Authentication System Using Multimodal Biometrics and MapReduce
AU - Divakar, Meghana A.
AU - Arakeri, Megha P.
N1 - Publisher Copyright:
© 2018, Springer Nature Singapore Pte Ltd.
PY - 2018
Y1 - 2018
N2 - Establishing the identity of a person with the use of individual biometric features has become the need for the present technologically advancing world. Due to rise in data thefts and identity hijacking, there is a critical need for providing user security using biometric authentication techniques. Biometrics is the science of recognizing a person by evaluating the distinguished physiological and biological traits of the person. A unimodal biometric system is known to have many disadvantages with regard to accuracy, reliability, and security. Multimodal biometric systems combine more than one biometric trait to identify a person in order to increase the security of the application. The proposed multimodal biometric system combines three biometric traits for individual authentication namely Face, Fingerprint, and Voice. MapReduce is the technique used for analyzing and processing big data sets that cannot fit into memory.
AB - Establishing the identity of a person with the use of individual biometric features has become the need for the present technologically advancing world. Due to rise in data thefts and identity hijacking, there is a critical need for providing user security using biometric authentication techniques. Biometrics is the science of recognizing a person by evaluating the distinguished physiological and biological traits of the person. A unimodal biometric system is known to have many disadvantages with regard to accuracy, reliability, and security. Multimodal biometric systems combine more than one biometric trait to identify a person in order to increase the security of the application. The proposed multimodal biometric system combines three biometric traits for individual authentication namely Face, Fingerprint, and Voice. MapReduce is the technique used for analyzing and processing big data sets that cannot fit into memory.
UR - https://www.scopus.com/pages/publications/85059953644
UR - https://www.scopus.com/inward/citedby.url?scp=85059953644&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-3920-1_8
DO - 10.1007/978-981-10-3920-1_8
M3 - Chapter
AN - SCOPUS:85059953644
T3 - Lecture Notes in Networks and Systems
SP - 71
EP - 82
BT - Lecture Notes in Networks and Systems
PB - Springer
ER -