New Modelling of Modified Two Dimensional Fisherface Based Feature Extraction
Abstract: Biometric researches
have been interesting field for many researches included facial recognition.
Crucial process of facial recognition is feature extraction. One Dimensional
Linear Discriminant Analysis is one of feature extraction method is development
of Principal Component Analysis mostly used by researches. But, it has
limitation, it can efficiently work when number of training sets greater or
equal than number of dimensions of image training set. This limitation has been
overcome by using Two Dimensional Linear Discriminant Analysis. However, search
value of matrix identity R and L by using Two Dimensional Linear Discriminant
Analysis takes high cost, which is O(n3). In this research, the seeking of
“Scatter between Class” and “Scatter within Class” by using Discriminant
Analysis without having to find the value of R and L advance are proposed. Time
complexity of proposed method is O(n2). Proposed method has been tested by
using AT&T face image database. The experimental results show that maximum recognition
rate of proposed method is 100%.
Keywords: Biometric, Face
Recognition, Principal Component Analysis, Two Dimensional Linear Discriminant
Analysis
Author: Arif Muntasa
Journal Code: jptkomputergg140037
