Optimal Features ASM Texture description based on Taylor series Grids centered at the landmarks for local analysis Non linear classifier (kNN) for inside-outside labeling inside outside B. van Ginneken, A.F. Frangi, J.J. Staal, B.M. ter Haar Romeny, and M.A. Viergever (2002) IEEE Transactions on Medical Imaging, 21(8):924–933

Optimal Features ASM Face is too complex for the proposed labeling  Thin zones generate profile variations  Classes unbalance in high curvature points kNN slow (set dependent) Image features dependent on rotation 1 2 2

Invariant Optimal Features ASM F.M. Sukno, S. Ordas, C. Butakoff, S. Cruz, and A.F. Frangi IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(7):1105–1117

Invariant Optimal Features ASM Distance-based labeling 180 profiles of man and women with IOF-ASM 1 2 4

Invariant Optimal Features ASM 1 2 Multi-valued neuron classifier  Single neuron  Very fast Appropriate combination of derivatives allows for invariance to rigid transformations i 0 1 k-1 k- 2 Z 5

Segmentation tests Experiments on images Point to curve error Point to point error

IOFASM vs ASM DatasetImagesError AR % Equinox % XM2VTS % 1 2 7

IOFASM vs ASM ASM IOF-ASM 1 2 8

Identity Verification: Texture Based on texture  Eigenfaces-like approach from the segmentation results 1 2 9

Identity Verification: Texture

Related work

Conclusions on IOF-ASM 1 2 By using more elaborate descriptions of the texture it is possible to increase the accuracy of ASMs  IOF-ASM provides a generic framework  Features are optimized for every landmark Allows for a trade off between accuracy and speed  Feature selection: –15% error / –50% time About 30% more accurate than ASM in facial feature localization  Derives in better identification rates Invariant to in-plane rotations 12

Out-of-plane Rotations Environment constraints ● Surveillance systems ● Car driver images ASM: ● Similarity does not remove 3D pose ● Multiple-view database Other approaches ● Non-linear models ● 3D models: multiple views Database