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Enhancing Texture Description in Local Analysis Using Optimized ASM for Facial Feature Localization

This work focuses on improving texture descriptions for Active Shape Models (ASM) by implementing an Invariant Optimal Features approach, enabling more accurate facial feature localization. The use of non-linear classifiers like kNN addresses challenges such as high curvature points and profile variances. Tests conducted on over 3,400 images revealed that the optimized features outperformed traditional ASM methods, achieving a 30% increase in localization accuracy while speeding up processing times. This research sets a framework for effective identity verification in various applications, especially in surveillance and driver monitoring.

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Enhancing Texture Description in Local Analysis Using Optimized ASM for Facial Feature Localization

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  1. Texture description based on Taylor series Grids centered at the landmarks for local analysis Non linear classifier (kNN) for inside-outside labeling outside inside 2 1 Optimal Features ASM B. van Ginneken, A.F. Frangi, J.J. Staal, B.M. terHaarRomeny, and M.A. Viergever (2002) IEEE Transactions on Medical Imaging, 21(8):924–933

  2. 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 2 1 Optimal Features ASM

  3. 2 1 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

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

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

  6. 2 1 Segmentation tests Experimentson 3400+ images Point to curve error Point to point error

  7. 2 1 IOFASM vs ASM

  8. 2 1 IOFASM vs ASM IOF-ASM ASM

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

  10. 2 1 Identity Verification: Texture

  11. 2 1 Related work

  12. 2 1 Conclusions on IOF-ASM • 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

  13. 2 1 3 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 AV@CAR Database

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