Face Recognition System by Integrating PCA, FLDA, Artificial Neural Networks and Minimum Euclidean Distance
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International Journal of Engineering Trends and Technology (IJETT) | 
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| © 2014 by IJETT Journal | ||
| Volume-15 Number-9 |
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| Year of Publication : 2014 | ||
| Authors : Shereena T K , Ashok Babu |
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 10.14445/22315381/IJETT-V15P282 |
Citation
Shereena T K , Ashok Babu. "Face Recognition System by Integrating PCA, FLDA, Artificial Neural Networks and Minimum Euclidean Distance", International Journal of Engineering Trends and Technology (IJETT), V15(9),429-433 Sep 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Intensive works has been performed over the topic of face recognition from the subject of computer vision and pattern recognition .Face recognition system works on the clue of the face geometry. Over several facial expressions; face recognition system gets an argumentative effect on the recognition process. Principal Component Analysis is being utilized as the most common feature extraction techniques in Face Recognition. It has been extensively employed for face identification. The face recognition system functions by projecting face image onto a characteristic space .The Most commonly each face recognition system uses one algorithm for feature extraction, but here it uses the combined techniques to achieve the maximum accuracy in the results. Principal Component Analysis and Fisher Linear Discriminant Algorithm are used for feature extraction. Images could be identified by the Artificial Neural Network and Minimum Euclidian Distance
References
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Keywords
Principal Component Analysis, Fisher Linear Discriminant Algorithm, Artificial Neural Network, Minimum Euclidian Distance