TY - GEN
T1 - Intra-facial-feature canonical correlation analysis for face recognition
AU - Chou, Yang Ting
AU - Yang, Jar Ferr
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/5
Y1 - 2016/1/5
N2 - In the real-world face recognition tasks, the limited size of facial images and the lack amount of training data would lead to fail the work or degrade the recognition accuracy dramatically. Face recognition approaches such as principal component analysis (PCA), linear discriminant analysis (LDA) and linear regression classification (LRC) are limited by the number of collected facial features. The traditional canonical correlation analysis (CCA) can address the relationship between two multivariate data sets that could be miss portion of important messages. In order to handle the insufficient collected facial information and improve the CCA, we propose intra-facial-features canonical correlation analysis (ICCA). The ICCA involves multiple sets of multivariate of different facial features that could be eyes, nose and mouth. Moreover, the proposed approach can also calculate the relationship among the intra facial features. Experimental results show that the proposed approach achieves better recognition rate than the traditional statistical analysis on the AR face database.
AB - In the real-world face recognition tasks, the limited size of facial images and the lack amount of training data would lead to fail the work or degrade the recognition accuracy dramatically. Face recognition approaches such as principal component analysis (PCA), linear discriminant analysis (LDA) and linear regression classification (LRC) are limited by the number of collected facial features. The traditional canonical correlation analysis (CCA) can address the relationship between two multivariate data sets that could be miss portion of important messages. In order to handle the insufficient collected facial information and improve the CCA, we propose intra-facial-features canonical correlation analysis (ICCA). The ICCA involves multiple sets of multivariate of different facial features that could be eyes, nose and mouth. Moreover, the proposed approach can also calculate the relationship among the intra facial features. Experimental results show that the proposed approach achieves better recognition rate than the traditional statistical analysis on the AR face database.
UR - http://www.scopus.com/inward/record.url?scp=84962159185&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962159185&partnerID=8YFLogxK
U2 - 10.1109/TENCON.2015.7372961
DO - 10.1109/TENCON.2015.7372961
M3 - Conference contribution
AN - SCOPUS:84962159185
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
BT - TENCON 2015 - 2015 IEEE Region 10 Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE Region 10 Conference, TENCON 2015
Y2 - 1 November 2015 through 4 November 2015
ER -