This paper introduces an experimental study on the recognition of the person's face by
utilizing three
Techniques of extraction: Principle Components Analysis (PCA), Linear Discriminant
Analysis (LDA) and Contourlet- Curvelet Transform (CCT). The results of these approaches
were observed and compared to discover the perfect scheme for identification of human faces.
The tests have been carried out on the
faces databases of (ORL) ,(UMIST), and (JAFFE). The results acquired by the methods were
quantified by altering the ratio of train to test photos in three categories: 75/25, 55/45 and
35/65. The evaluation results showed that the CCT extraction method provides better results
than the others. The highest recognition rate was recorded for the CCT approach (recognition
rate=98.980%) when the (train /test) photos ratio is (75/25). Furthermore, the best recognition
rates for the LDA and PCA were 96.391% and 95.127% respectively. The Matlab R2019b
program was used for implementing and testing the algorithms.
Publication Date
1/21/2021
Publisher
IOP Conference Series: Materials Science and Engineering
احمد هيثم نجم | AHMED HAITHAM NAJIM | 2653