Active shape models—their training and application
Computer Vision and Image Understanding
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Integration of Eigentemplate and Structure Matching for Automatic Facial Feature Detection
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face recognition by elastic bunch graph matching
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
A Comparison of Gabor Filter Methods for Automatic Detection of Facial Landmarks
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Feature-Based Affine-Invariant Localization of Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Landmark Paper in Face Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Locating Facial Features with an Extended Active Shape Model
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Independent component analysis and support vector machine for face feature extraction
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
The BANCA database and evaluation protocol
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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
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Automatic facial landmark location is a difficult challenge for realistic face recognition applications, where the face is recorded under variable illumination conditions including indoor and outdoor recordings and also with some pose and scale variability. Moreover, the image distortion and complex background also bring some difficulty both for landmark location and face recognition. The proposed landmark detection method, called Combined Active Shape Models, is robust to illumination, translation, and rotation. It exploits the Scale Invariant Feature Transform (SIFT) [1] and the Active Shape Model (ASM) [2]. In order to have a better representation of face images, the landmarks on the face region and the face contour are modeled and processed separately. The performance of the proposed Combined-ASM algorithm is tested on the BioID and FRGCv2.0 face image databases.