Face Alignment Using Boosting and Evolutionary Search

In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the face alignment problem as a process of maximizing the response from a boosting classifier. Enlightened by AAM and BAM, we present a framework which implements improved boosting classifiers based on more discriminative features and exhaustive search strategies. In this paper, we utilize granular features to replace the conventional rectangular Haar-like features, to improve discriminability, computational efficiency, and a larger search space. At the same time, we adopt the evolutionary search process to solve the deficiency of searching in the large feature space. Finally, we test our approach on a series of challenging data sets, to show the accuracy and efficiency on versatile face images.

[1]  Trevor Hastie,et al.  Additive Logistic Regression : a Statistical , 1998 .

[2]  Daniel Howard,et al.  Evolution of Ship Detectors for Satellite SAR Imagery , 1999, EuroGP.

[3]  Harry Shum,et al.  Accurate Face Alignment using Shape Constrained Markov Network , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  Timothy F. Cootes,et al.  Trainable method of parametric shape description , 1992, Image Vis. Comput..

[5]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[6]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[7]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[8]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Yi Zhou,et al.  Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[11]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

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

[13]  Xiaoming Liu,et al.  Generic Face Alignment using Boosted Appearance Model , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Fabien Moutarde,et al.  COMBINING ADABOOST WITH A HILL-CLIMBING EVOLUTIONARY FEATURE SEARCH FOR EFFICIENT TRAINING OF PERFORMANT VISUAL OBJECT DETECTORS , 2006 .

[15]  Yuan Li,et al.  High-Performance Rotation Invariant Multiview Face Detection , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Rong Xiao,et al.  Dynamic Cascades for Face Detection , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[18]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Andreas Zell,et al.  Combining Adaboost learning and evolutionary search to select features for real-time object detection , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).