| dc.description.abstract | Face recognition is one of many important researches and many applications have implemented it. Two different techniques have been proposed for face recognition. There are feature-based systems and image-based systems. The objective of this research is to compare two simple strategies between the systems. The first system is based on a set of geometrical features such as nose width and length, mouth position, and the second one is based on almost grey-level pixel based. The data are faces of 20 people: 3 females and 17 males with 6 images each. Face data are usually difficult to recognize, classify and analysis. This research uses as Principal Component Analysis (PCA) method to reduce face’s data. K-Nearest Neighbor algorithm is used to examine the face data’s accurate and validation from both methods.The results obtained on testing sets is about 60% correct using geometrical features and perfect recognition using image-base system is about 95%. Noise is also used in this research with several varians such as 0,02 , 0,10 , and 0,20. It is difficult to recognise image with high varians of Salt and Pepper noise in image-based systems. | en |
