Optimizing Face Recognition Using PCA

Abdullah, Manal; Wazzan, Majda; Bosaeed, Sahar

doi:10.5121/ijaia.2012.3203

Cited by 35 publications

(24 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of the individuals are between 18 and 20 years old. The lighting is artificial and some of the images are captured with glasses, and a mixture of tungsten and fluorescent overhead [7]. Figure 3 shows a sample images of Face94 database.…”

Section: B Face94 Databasementioning

confidence: 99%

Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance

Barnouti¹,

Al-Dabbagh²,

Matti³

et al. 2016

ijacsa

View full text Add to dashboard Cite

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.

show abstract

Section: B Face94 Databasementioning

confidence: 99%

Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance

Barnouti¹,

Al-Dabbagh²,

Matti³

et al. 2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…City Block Distance, which is also known as L1 Distance or Manhattan Distance classifier is the summation of the absolute difference between two vectors [23]. It is especially useful for discrete types of descriptions.…”

Section: City Block Distancementioning

confidence: 99%

“…This database is a part of a collection of face images. The collection contains four folders of images, namely, face94, face95, face96, and grimace [23]. The images are stored in 24-bit RGB and JPEG formats, and the camera used is a S-VHS camcorder.…”

Section: Face94 Databasementioning

confidence: 99%

“…(Abdullah et al, 2012) [23] performed three experements to improve PCA performance by decreasing the computational time while same performance. A first experiment is performed to find the best number of images for each person to be used in the training set that gives a highest recognition rate.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…Euclidean Distance is defined as the straight line distance between two points, which examines the root of square differences between the coordinates of a pair of subjects [23]. A minimum Euclidean Distance classifier is the most favorable condition for normally distributed classes [21].…”

Section: Euclidean Distancementioning

confidence: 99%

See 2 more Smart Citations

Face Recognition: A Literature Review

Barnouti¹,

Al-Dabbagh²,

Matti³

2016

IJAIS

View full text Add to dashboard Cite

Face recognition have gained a great deal of popularity because of the wide range of applications such as in entertainment, smart cards, information security, law enforcement, and surveillance. It is a relevant subject in pattern recognition, computer vision, and image processing. Two major methods are used for features extraction, which can be classified into appearance-based and Model-based methods.Appearance-based methods use global representations to identify a face. Model-based face methods aim to construct a model of the human face that capture facial variations. Image similarity is the distance between the vectors of two images. This paper contains Four sections. The first section discusses face recognition applications with examples. The second section discuss the common feature face recognition methods. The third section discuss distance measurement classifiers. The fourth section discuss different face recognition databases.

show abstract