Active Shape Models with Invariant Optimal Features: Application to Facial Analysis

This work is framed in the field of statistical face analysis. In particular, the problem of accurate segmentation of prominent features of the face in frontal view images is addressed. We propose a method that generalizes linear active shape models (ASMs)l which have already been used for this task. The technique is built upon the development of a nonlinear intensity model, incorporating a reduced set of differential invariant features as local image descriptors. These features are invariant to rigid transformations, and a subset of them is chosen by sequential feature selection for each landmark and resolution level. The new approach overcomes the unimodality and Gaussianity assumptions of classical ASMs regarding the distribution of the intensity values across the training set. Our methodology has demonstrated a significant improvement in segmentation precision as compared to the linear ASM and optimal features ASM (a nonlinear extension of the pioneer algorithm) in the tests performed on AR, XM2VTS, and EQUINOX databases.

[1]  Timothy F. Cootes,et al.  Correspondence Using Distinct Points Based on Image Invariants , 1997, BMVC.

[2]  Timothy F. Cootes,et al.  Comparing Active Shape Models with Active Appearance Models , 1999, BMVC.

[3]  Diego A. Socolinsky,et al.  Appearance-Based Facial Recognition Using Visible and Thermal Imagery: A Comparative Study , 2006 .

[4]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[5]  Timothy F. Cootes,et al.  A Comparison of Face Verification Algorithms using Appearance Models , 2002, BMVC.

[6]  Cordelia Schmid,et al.  Local Grayvalue Invariants for Image Retrieval , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Jouko Lampinen,et al.  A Bayesian Occlusion Model for Sequential Object Matching , 2004, BMVC.

[8]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[9]  Timothy F. Cootes,et al.  Improving Appearance Model Matching Using Local Image Structure , 2003, IPMI.

[10]  A. Martínez,et al.  The AR face databasae , 1998 .

[11]  Mikkel B. Stegmann,et al.  The IMM Face Database, An Annotated Dataset of 240 Face Images , 2004 .

[12]  Timothy F. Cootes,et al.  A comparison of shape constrained facial feature detectors , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[13]  Timothy F. Cootes,et al.  Active Shape Models-Their Training and Application , 1995, Comput. Vis. Image Underst..

[14]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[15]  Vytautas Perlibakas,et al.  Distance measures for PCA-based face recognition , 2004, Pattern Recognit. Lett..

[16]  Joos Vandewalle,et al.  Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications , 2012 .

[17]  Luc Vandendorpe,et al.  Face Verification Competition on the XM2VTS Database , 2003, AVBPA.

[18]  Mineichi Kudo,et al.  Comparison of algorithms that select features for pattern classifiers , 2000, Pattern Recognit..

[19]  Alejandro F. Frangi,et al.  Active shape model segmentation with optimal features , 2002, IEEE Transactions on Medical Imaging.

[20]  Timothy F. Cootes,et al.  Statistical models of appearance for computer vision , 1999 .

[21]  Alejandro F. Frangi,et al.  Homographic active shape models for view-independent facial analysis , 2005, SPIE Defense + Commercial Sensing.

[22]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[23]  Constantine Butakoff,et al.  Blurred image restoration using the type of blur and blur parameter identification on the neural network , 2002, IS&T/SPIE Electronic Imaging.

[24]  Joos Vandewalle,et al.  Multi-Valued and Universal Binary Neurons , 2000 .

[25]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[26]  Sunil Arya,et al.  An optimal algorithm for approximate nearest neighbor searching fixed dimensions , 1998, JACM.

[27]  J. E. Porter,et al.  Normalizations and selection of speech segments for speaker recognition scoring , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[28]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[29]  Sharath Pankanti,et al.  Error analysis of pattern recognition systems - the subsets bootstrap , 2004, Comput. Vis. Image Underst..

[30]  Ethem Alpaydin.  Multiple neural networks and weighted voting , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[31]  Timothy F. Cootes,et al.  Automatic Interpretation and Coding of Face Images Using Flexible Models , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Alan Wee-Chung Liew,et al.  Segmentation of color lip images by spatial fuzzy clustering , 2003, IEEE Trans. Fuzzy Syst..

[33]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[34]  Alejandro F. Frangi,et al.  Active Shape Models with Invariant Optimal Features (IOF-ASMs) , 2005, AVBPA.

[35]  Peter Y. K. Cheung,et al.  Adaptive automatic facial feature segmentation , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[36]  Samy Bengio,et al.  A statistical significance test for person authentication , 2004, Odyssey.

[37]  Jiri Matas,et al.  XM2VTSDB: The Extended M2VTS Database , 1999 .

[38]  H. Damasio,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence: Special Issue on Perceptual Organization in Computer Vision , 1998 .