DocumentCode
1615202
Title
3D face recognition based on feature detection using active shape models
Author
Park, Sang-Jun ; Shin, Dong-Won
Author_Institution
Dept. of Mech. Eng., Kumoh Nat. Inst. of Technol., Gumi
fYear
2008
Firstpage
1881
Lastpage
1886
Abstract
This paper represents a method that detects facial features and normalizes 3D range images and 2D images for full automatic face recognition. The active shape models or ASM is applied to extract the position of the eyes, the nose and the mouth. The approximate position of the face in the image is detected using the projection method and the facial profile on the 3D range image so that the initial position of the ASM model is set on the image before the ASM searching. The 3D range image is rotated facing front view using feature points. Then, cropped inside of the sphere to remove none facial parts. The face data is rearranged to fit on the desired frame which size is 201times151. The shape model is built up of 50 images from 10 individuals and the face recognition system is evaluated on 300 images from 30 individuals. The PCA-based hybrid classifier is applied to design the face recognition system with good results.
Keywords
face recognition; feature extraction; image classification; principal component analysis; shape recognition; solid modelling; 3D face recognition; 3D range image; PCA-based hybrid classifier; active shape model; feature detection; image alignment; pose variation; projection method; Active shape model; Computer vision; Face detection; Face recognition; Facial features; Iterative algorithms; Mechanical engineering; Mouth; Nose; Principal component analysis; 3D face recognition; Active shape models(ASM); Alignment; Hybrid Classifier; PCA; Pose variation;
fLanguage
English
Publisher
ieee
Conference_Titel
Control, Automation and Systems, 2008. ICCAS 2008. International Conference on
Conference_Location
Seoul
Print_ISBN
978-89-950038-9-3
Electronic_ISBN
978-89-93215-01-4
Type
conf
DOI
10.1109/ICCAS.2008.4694404
Filename
4694404
Link To Document