A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
Generalized gradient vector flow external forces for active contours
Signal Processing - Special issue on deformable models and techniques for image and signal processing
Face Recognition through Geometrical Features
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Enhancing Human Face Detection Using Motion and Active Contours
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Snake Head Boundary Extraction Using Global and Local Energy Minimisation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Automatic snakes for robust lip boundaries extraction
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Automatic real time localization of frowning and smiling faces under controlled head rotations
WSEAS Transactions on Signal Processing
Facial feature detection using distance vector fields
Pattern Recognition
Automatic extraction of face contours in images and videos
Future Generation Computer Systems
Facial-feature detection and localization based on a hierarchical scheme
Information Sciences: an International Journal
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The approach based on the mathematical morphology and the variational calculus is presented for the detection of an exact face contour in still grayscale images. The facial features (eyes and lips) are detected by using the mathematical morphology and the heuristic rules. Using these features an image is filtered and an edge map is prepared. The face contour is detected by minimizing its internal and external energy. The internal energy is defined by the contour tension and the rigidity. The external energy is defined by using the generalized gradient vector flow field of the image edge map. Initial contour is calculated using the detected face features. The contour detection experiments were performed using the database of 427 face images. Automatically detected contours were compared with manually labeled contours using an area and the Euclidean distance-based error measures.