Title :
An advance approach Of PCA for gender recognition
Author :
Kumar, Ajit ; Rawat, Karun ; Gupta, Deepika
Author_Institution :
Gov. Eng. Coll., Ajmer, India
Abstract :
This paper focuses on mathematical rigor to provide explicit solution for gender recognition by extracting feature vector. This paper implement face recognition system using Principal Component Analysis (PCA) algorithm. In addition by using face-rec database we will use kernel SVM to find k Eigen for which error of classification is smallest then project data points along these vector to reduce dimensionality.
Keywords :
face recognition; feature extraction; gender issues; image classification; principal component analysis; support vector machines; visual databases; PCA algorithm; advance PCA approach; classification error; dimensionality reduction; face recognition system; face-rec database; feature vector extraction; gender recognition; k Eigen; kernel SVM; principal component analysis algorithm; Face; Face recognition; Feature extraction; Principal component analysis; Support vector machines; Training; Vectors; Eigen faces; Euclidian distance; Gender Recognition; Principal Component Analysis; SVM;
Conference_Titel :
Information Communication and Embedded Systems (ICICES), 2013 International Conference on
Conference_Location :
Chennai
Print_ISBN :
978-1-4673-5786-9
DOI :
10.1109/ICICES.2013.6508195
