Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance
| Autor: | Mustafa Abdul Sahib Naser, Nawaf Hazim Barnouti, Sinan Sameer Mahmood Al-Dabbagh, Wael Esam Matti |
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| Rok vydání: | 2016 |
| Předmět: |
General Computer Science
business.industry Computer science 3D single-object recognition Dimensionality reduction Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020207 software engineering Pattern recognition 02 engineering and technology Linear discriminant analysis Facial recognition system Object detection Euclidean distance Object-class detection Eigenface 0202 electrical engineering electronic engineering information engineering Three-dimensional face recognition 020201 artificial intelligence & image processing Viola–Jones object detection framework Computer vision Artificial intelligence Face detection business |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 7 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2016.070550 |
| Popis: | In this paper, an automatic face recognition system is proposed based on appearance-based features that focus on the entire face image rather than local facial features. The first step in face recognition system is face detection. Viola-Jones face detection method that capable of processing images extremely while achieving high detection rates is used. This method has the most impact in the 2000’s and known as the first object detection framework to provide relevant object detection that can run in real time. Feature extraction and dimension reduction method will be applied after face detection. Principal Component Analysis (PCA) method is widely used in pattern recognition. Linear Discriminant Analysis (LDA) method that used to overcome drawback the PCA has been successfully applied to face recognition. It is achieved by projecting the image onto the Eigenface space by PCA after that implementing pure LDA over it. Square Euclidean Distance (SED) is used. The distance between two images is a major concern in pattern recognition. The distance between the vectors of two images leads to image similarity. The proposed method is tested on three databases (MUCT, Face94, and Grimace). Different number of training and testing images are used to evaluate the system performance and it show that increasing the number of training images will increase the recognition rate. |
| Databáze: | OpenAIRE |
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