TY - GEN
T1 - Face recognition system for smartphone based on LBP
AU - Olivares-Mercado, Jesus
AU - Toscano-Medina, Karina
AU - Sanchez-Perez, Gabriel
AU - Perez-Meana, Hector
AU - Nakano-Miyatake, Mariko
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/26
Y1 - 2017/5/26
N2 - This paper presents a face recognition algorithm based on Local Binary Pattern (LBP) to be implemented in a Smartphone with Android operating system where the input image is obtained using the camera of such Smartphone. The LBP algorithm is used for Face characterization, due to its low complexity and its robustness light of this method is chosen to be applied in a Smartphone, this is because the light sensor of smartphone could darken or lighten the captured image and affect a efficient recognition. To perform system testing on a Smartphone was used a standard database (AR Face database) to simulate the capture of images, the average of images was used for obtaining a template by person and using Euclidean distance for classification, showing that the LBP obtains good results using a simple classification algorithm with a Smartphone with limited processing power like a smartphone, further tests were performed with 1 to 9 training images, obtaining up to 90% of recognition.
AB - This paper presents a face recognition algorithm based on Local Binary Pattern (LBP) to be implemented in a Smartphone with Android operating system where the input image is obtained using the camera of such Smartphone. The LBP algorithm is used for Face characterization, due to its low complexity and its robustness light of this method is chosen to be applied in a Smartphone, this is because the light sensor of smartphone could darken or lighten the captured image and affect a efficient recognition. To perform system testing on a Smartphone was used a standard database (AR Face database) to simulate the capture of images, the average of images was used for obtaining a template by person and using Euclidean distance for classification, showing that the LBP obtains good results using a simple classification algorithm with a Smartphone with limited processing power like a smartphone, further tests were performed with 1 to 9 training images, obtaining up to 90% of recognition.
UR - http://www.scopus.com/inward/record.url?scp=85021787929&partnerID=8YFLogxK
U2 - 10.1109/IWBF.2017.7935111
DO - 10.1109/IWBF.2017.7935111
M3 - Contribución a la conferencia
AN - SCOPUS:85021787929
T3 - Proceedings - 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017
BT - Proceedings - 2017 5th International Workshop on Biometrics and Forensics, IWBF 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Workshop on Biometrics and Forensics, IWBF 2017
Y2 - 4 April 2017 through 5 April 2017
ER -