Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic algorithms for object recognition in a complex scene
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Genetic Block Matching Algorithm for Video Coding
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
A Non-Linear Gray-Level Appearance Model Improves Active Shape Model Segmentation
MMBIA '01 Proceedings of the IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA'01)
Deformable templates for face recognition
Journal of Cognitive Neuroscience
Locating and extracting the eye in human face images
Pattern Recognition
Automatic facial feature extraction by genetic algorithms
IEEE Transactions on Image Processing
Facial feature localization based on an improved active shape model
Information Sciences: an International Journal
Efficient Facial Features Warping Using BSM (Bayesian Shape Model)
ICCSA '08 Proceedings of the international conference on Computational Science and Its Applications, Part II
The Performance of Two Deformable Shape Models in the Context of the Face Recognition
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
Multi-view face segmentation using fusion of statistical shape and appearance models
Computer Vision and Image Understanding
Customer-dependent storytelling tool with authoring and viewing functions
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Contour extraction of facial feature components using template based snake algorithm
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
A novel ASM-based two-stage facial landmark detection method
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
A graphical model based solution to the facial feature point tracking problem
Image and Vision Computing
Facial expression feature selection based on rough set
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
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The active shape model (ASM) has been used successfully to extract the facial features of a face image under frontal view. However, its performance degrades when the face concerned is under perspective variations. In this paper, a modified shape model is proposed which can adapt to face images under different orientations. To make the model represent a face more flexibly, the representations of the important facial features, i.e. the eyes, nose and mouth, and the face contour are separated. An energy function is defined that links up these two representations of a human face. In order to represent a face image under different poses, three models are employed to represent the important facial features: the left-viewed, right-viewed, and frontal-viewed models. The genetic algorithm (GA) is applied to search for the best representation of face images. Experimental results demonstrate that our proposed method can achieve a better performance in representing face images under different perspective variations and facial expressions than the conventional ASM can.