Classification of face images using discrete cosine transform

Author

Karhan, Z. ; Ergen, B.

Author_Institution

Bilgisayar Muhendisligi Bolumu, Firat Univ., Elazig, Turkey

fYear

2013

fDate

24-26 April 2013

Firstpage

1

Lastpage

4

Abstract

In this study, it is aimed to determine whether a given image belongs to for that person. For feature extraction, which is an important part of pattern recognition, feature vector is obtained by using discrete cosine transform after performing preprocess the images on the current face. Based on the of datas obtained from conversion are classified by using 5%, 8%, 10%, and 15%. The nearest neighbor algorithm (KNN) is used in classification process. Face images consist of images that, taken from ORL database, belongs to 40 individuals, each has 10 different images. As a result, high success were obtained by using the few data.

Keywords

discrete cosine transforms; face recognition; feature extraction; image classification; KNN; ORL database; discrete cosine transform; face images classification; feature extraction; feature vector; nearest neighbor algorithm; pattern recognition; Abstracts; Classification algorithms; Discrete cosine transforms; Face; Feature extraction; Pattern recognition; Support vector machine classification; K NN Classifier; Pattern recognition; discrete cosinüs transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference (SIU), 2013 21st

Conference_Location

Haspolat

Print_ISBN

978-1-4673-5562-9

Electronic_ISBN

978-1-4673-5561-2

Type

conf

DOI

10.1109/SIU.2013.6531364

Filename

6531364