Active shape models—their training and application
Computer Vision and Image Understanding
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
An Improved Active Shape Model for Face Alignment
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Accurate Active Shape Model for Face Alignment
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Facial Features Extraction in Color Images Using Enhanced Active Shape Model
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
Unconstrained iris acquisition and recognition using COTS PTZ camera
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Pre-organizing Shape Instances for Landmark-Based Shape Correspondence
International Journal of Computer Vision
An interactive tool for extremely dense landmarking of faces
Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications
Age invariant face verification with relative craniofacial growth model
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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In this paper we present an improved method for locating facial landmarks in images containing frontal faces using a modified Active Shape Model. Our main contributions include the use of an optimal number of facial landmark points, better profiling methods during the fitting stage and the development of a more suitable optimization metric to determine the best location of the landmarks compared to the simplistic minimum Mahalanobis distance criteria used to date. We build a subspace to model variations of appearance around each facial landmark and use this subspace to enhance the accuracy of the fitting process around each landmark. This enhancement provides a significant improvement in fitting and simultaneously determines which points were poorly fitted using reconstruction error, thus allowing for automatic correction or interpolation of any poorly fitted points. Our implementation, with the above mentioned improvements, leads to extremely accurate results even when dealing with faces with expressions, slight pose variations and in-plane rotations. Experiments conducted on test sets drawn from three databases (NIST Multiple Biometric Grand Challenge-2008 (MBGC-2008), CMU Multi-PIE and the Japanese Female Facial Expression (JAFFE) database) show that our proposed approach leads to far better performance compared to the classical Active Shape Model of Cootes et al. and other traditional methods and provides a robust automatic facial landmark annotation which is the first critical step in face registration, pose correction and face recognition.