scholarly journals Facial features localization in front view head and shoulders images

Author(s):  
A.M. Alattar ◽  
S.A. Rajala
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.


Author(s):  
V. Gorbatsevich ◽  
Yu. Vizilter ◽  
V. Knyaz ◽  
S. Zheltov

A technique for automated face detection and its pose estimation using single image is developed. The algorithm includes: face detection, facial features localization, face/background segmentation, face pose estimation, image transformation to frontal view. Automatic face/background segmentation is performed by original graph-cut technique based on detected feature points. The precision of face orientation estimation based on monocular digital imagery is addressed. The approach for precision estimation is developed based on comparison of synthesized facial 2D images and scanned face 3D model. The software for modelling and measurement is developed. The special system for non-contact measurements is created. Required set of 3D real face models and colour facial textures is obtained using this system. The precision estimation results demonstrate the precision of face pose estimation enough for further successful face recognition.


2009 ◽  
Vol 35 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Li-Ting WANG ◽  
Xiao-Qing DING ◽  
Chi FANG

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