Abstract
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.
Highlights
Face detection is among the important advanced topics in computer vision and pattern recognition communities and it is the first important step for facial analysis methods and among the most important issues in computer vision like face recognition, facial expression, head tracking, face verification
Face recognition basically divided into three steps begins with face detection continue with feature extraction and end up with distance measurement process
Milborrow/University of Cape Town (MUCT), Face94, and Grimace databases are used in this work
Summary
Face detection is among the important advanced topics in computer vision and pattern recognition communities and it is the first important step for facial analysis methods and among the most important issues in computer vision like face recognition, facial expression, head tracking, face verification. Face recognition methods can be divided into appearancebased or model-based methods. Appearance-based (Holistic) face recognition legally attempt to identify faces using global representations based on the entire image rather than local facial features. Appearance-based methods can be classified as either linear or non-linear. Linear www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 7, No 5, 2016 appearance-based methods perform a linear dimension reduction [4]. Non-linear appearance-based methods are usually more complicated than linear methods. Model-based face recognition scheme aims to construct a model of the human face that can capture facial variations. It can be either 2-Dimensional or 3-Dimensional. Regardless of the method, the most important concern in face recognition is dimensionality. Computational complexity is an important problem when working on large database
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call