Facial feature extraction /
Muhammad Marzuq Mohd Sharip
Facial feature extraction / Facial feature extraction [computer file] Muhammad Marzuq Mohd Sharip - Kuantan, Pahang : UMP, 2009 - xiv, 50 p. : ill. (some col.) ; 30 cm. + 1 computer disc
Project paper (Bachelor of Electrical Engineering (Control and Instrumentation)) -- Universiti Malaysia Pahang - 2009
Bibliography : p.[40]- 46
This project presents the facial feature extraction system and face recognition system. The test image that used for this project contain various type. There are ten different images of each of 40 disntinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions; open or closed eyes, smiling or not smiling, and facial details; glasses or no glasses. All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position with tolerance for some side movement. For the facial feature extraction system, it is more focus on eye extraction. The eye will extracted from the face by finding the centroid of the eye region using threshold technique. For the recognition system, Principle Component Analysis (PCA) is used to match the test image with the database image. The system will find which database image has a maximum percentage based on similarity of the pattern of the image
THE0008085(Local)
Biometric identification
Human face recognition (Computer science)
Facial feature extraction / Facial feature extraction [computer file] Muhammad Marzuq Mohd Sharip - Kuantan, Pahang : UMP, 2009 - xiv, 50 p. : ill. (some col.) ; 30 cm. + 1 computer disc
Project paper (Bachelor of Electrical Engineering (Control and Instrumentation)) -- Universiti Malaysia Pahang - 2009
Bibliography : p.[40]- 46
This project presents the facial feature extraction system and face recognition system. The test image that used for this project contain various type. There are ten different images of each of 40 disntinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions; open or closed eyes, smiling or not smiling, and facial details; glasses or no glasses. All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position with tolerance for some side movement. For the facial feature extraction system, it is more focus on eye extraction. The eye will extracted from the face by finding the centroid of the eye region using threshold technique. For the recognition system, Principle Component Analysis (PCA) is used to match the test image with the database image. The system will find which database image has a maximum percentage based on similarity of the pattern of the image
THE0008085(Local)
Biometric identification
Human face recognition (Computer science)