Abstract
Face recognition is a special pattern recognition for faces that compare input image with data in database. The image has a variety and has large dimensions, so that dimension reduction is needed, one of them is Principal Component Analysis (PCA) method. Dimensional transformation on image causes vector space dimension of image become large. At present, a feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. Classification process in 2DPCA using K-Nearest Neighbor (KNN) method by counting euclidean distance. In PCA method, face matrix is changed into one-dimensional matrix to get covariance matrix. While in 2DPCA, covariance matrix is directly obtained from face image matrix. In this research, we conducted 4 trials with different amount of training data and testing data, where data is taken from AT&T database. In 4 time testing, accuracy of 2DPCA+KNN method is higher than PCA+KNN method. Highest accuracy of 2DPCA+KNN method was obtained in 4th test with 96.88%. while the highest accuracy of PCA+KNN method was obtained in 4th test with 89.38%. More images used as training data compared to testing data, then the accuracy value tends to be greater.
Highlights
The rapid development of information technology has made a lot changes to human life [1]
A feature extraction technique called Two-Dimensional Principal Component Analysis (2DPCA) is proposed to overcome weakness of PCA. 2. Two-Dimensional Principal Component Analysis (2DPCA) has better computing time performance than PCA because covariance matrix is directly obtained from face image matrix and it is not necessary to transform matrix into one-dimensional vectors like on PCA method [9]
In PCA method, face matrix is converted into onedimensional matrix to get covariance matrix
Summary
The rapid development of information technology has made a lot changes to human life [1]. Development of information and technology computers can solve problems that occur in society [2]. Improvement of computer technology and telecomunications help many peoples to get a lot of work to done quickly, accurately, and efficiently [3]. Development of digital image processing is increasingly widespread, one of them is pattern recognition in digital imagery. Pattern is defined entities and can be identified through their features. More precise of recognition this method can do. The characteristics of identification results will be used to distinguish a pattern from other patterns
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