Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
Neural Networks and Rules-based Systems used to Find Rational and
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Dense feature extraction is becoming increasingly popular in face recognition. Face recognition is a vital component for authorization and security. In earlier days, CCA (Canonical Correlation Analysis) and SIFT (Scale Invariant Feature Transforms) was used for face recognition. Since multi scale extraction is not possible with these existing methods, a new approach to dense feature extraction is developed in this project. The proposed method combines dense feature extraction and decision based propagation neural network (DBPNN). Neural network algorithm is presented to recognize the face at different angle, and it is used for training and learning and leading to efficient and robust face recognition. Finally Iris matching is done by using Iterative randomized Hough transform for detecting the pupil region with number of iteration counts. Experimental results show that the proposed method is providing effective recognition rate with accuracy in comparing with existing methods.
S. Revathy. 2016. \u201cDetecting and Recognizing the Face and Iris Features from a Video Sequence using DBPNN and Adaptive Hamming Distance\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 16 (GJRE Volume 16 Issue F5): .
Crossref Journal DOI 10.17406/gjre
Print ISSN 0975-5861
e-ISSN 2249-4596
The methods for personal identification and authentication are no exception.
Total Score: 101
Country: India
Subject: Global Journal of Research in Engineering - F: Electrical & Electronic
Authors: S. Revathy (PhD/Dr. count: 0)
View Count (all-time): 173
Total Views (Real + Logic): 3731
Total Downloads (simulated): 1768
Publish Date: 2016 09, Sat
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Dense feature extraction is becoming increasingly popular in face recognition. Face recognition is a vital component for authorization and security. In earlier days, CCA (Canonical Correlation Analysis) and SIFT (Scale Invariant Feature Transforms) was used for face recognition. Since multi scale extraction is not possible with these existing methods, a new approach to dense feature extraction is developed in this project. The proposed method combines dense feature extraction and decision based propagation neural network (DBPNN). Neural network algorithm is presented to recognize the face at different angle, and it is used for training and learning and leading to efficient and robust face recognition. Finally Iris matching is done by using Iterative randomized Hough transform for detecting the pupil region with number of iteration counts. Experimental results show that the proposed method is providing effective recognition rate with accuracy in comparing with existing methods.
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