International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
www.irjet.net
Technique to Hybridize Principle Component and Independent Component Algorithms using Score Based Fusion Process Rupinder Kaur1 , Dr. Pardeep Kumar2 , Ms. Shalini Aggarwal3 1Department
of Computer Science & Applications, Kurukshetra University, Kurukshetra professor, Department of Computer & Application, Kurukshetra University, Kurukshetra 3Assistant professor, Computer Science,GCW, Karnal -----------------------------------------------------------------------***-------------------------------------------------------------------2Assistant
Abstract – The performance gains presented by these new
descriptors have led to significant growth in applying texture methods to a large variety of computer vision problems. In this paper, a hybrid face recognition approach has been used. Hybrid approach has a unique significance in Face Recognition Systems. They join different face detection techniques to achieve a better result as compared to single method. This paper presents hybridization between two face recognition techniques i.e. principle component analysis and independent component analysis. A score based approach has been used as a combiner process to hybrid these face recognition techniques. The experimented results show that the hybrid system has higher score value than face recognition systems using single method. Key Words: Face Recognition, PCA, ICA, BPNN and Score
based strategy.
A characteristic face recognition system includes the following steps: 1.
From dataset of images Evaluate the face area ,i.e. identify and position face
2.
Find a appropriate illustration of the face feature extraction; and
3.
Categorize the representations.
It is supposed that human face has been evaluated from dataset images with the help of methods that are mention in [2]. The intend of this study is focus on only steps 2 & 3.
2. Proposed Face Recognition Technique Algorithm PCA and ICA
1.INTRODUCTION The surveillance became a big challenging problem in the present world. Sake of security purpose in phone, banks or other public places there are different number of security systems such as password, finger prints and pattern recognitions. The pattern or passwords used can be trapped easily once if the user or the pattern is well known. The finger print system doesn’t achieve full-fledged result the through put is low because of the miss matches or a layer of distraction due to external sources and many other reasons. To provide a proper surveillance we are going for face recognition, here unique features of each individual are taken into consideration.
Face recognition system generally uses only one feature extraction method and one classifier. Normally as a classifier neural network are used called conventional method known as Single Feature Neural Network (SFNN) face recognition as shown in Fig. 1. Input Image
Preprocessing
Feature extraction
Classifier
known unknown person Fig.1: SFNN Face recognition system
Face recognition concept of feature [1] extraction and detection, is a small capacity for human beings. Human have [1] developed this skill to correctly and instantaneously recognize effects around us after millions of years of evolution. Implementations in computers are much more difficult task although not impossible.
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