Robust Head Pose Estimation with a New Principal Optimal Tradeoff Filter

One of the challenging problems encountered by face recognition is the difficulty in tackling pose variations, where fast and reliable head pose estimation is an essential step. In this paper, based on the correlation filter technique, a novel feature extraction framework, i.e., directional correlation filter set (DCFS), is developed for robust head pose estimation. In this framework, a principal optimal tradeoff filter (called POTF) is designed in the feature subspace obtained according to principal component analysis (PCA). Compared with the traditional methods that rely on the exact localization of facial feature points, our proposed method exploits the 1D frequency domain of the training data by using the correlation filter technique, which can capture the high-order statistics of a face for effective head pose estimation. Experimental results on several public face databases with large pose variations, including PIE, HPI, and UMIST, show the promising performance obtained by the proposed method on head pose estimation.

[1]  Rama Chellappa,et al.  Automatic head pose estimation using randomly projected dense SIFT descriptors , 2012, 2012 19th IEEE International Conference on Image Processing.

[2]  B. V. Vijaya Kumar,et al.  Minimum-variance synthetic discriminant functions , 1986 .

[3]  Mark J. T. Smith,et al.  A new directional filter bank for image analysis and classification , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[4]  Marios Savvides,et al.  Correlation Pattern Recognition for Face Recognition , 2006, Proceedings of the IEEE.

[5]  P. Réfrégier.  Filter design for optical pattern recognition: multicriteria optimization approach. , 1990, Optics letters.

[6]  B. V. K. Vijaya Kumar,et al.  Performance of composite correlation filters in fingerprint verification , 2004 .

[7]  Peyman Milanfar,et al.  A Tour of Modern Image Filtering: New Insights and Methods, Both Practical and Theoretical , 2013, IEEE Signal Processing Magazine.

[8]  Qiang Ji,et al.  3D Face pose estimation and tracking from a monocular camera , 2002, Image Vis. Comput..

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

[10]  Luc Van Gool,et al.  Real-time facial feature detection using conditional regression forests , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  B.V.K. Vijaya Kumar,et al.  Palmprint recognition using correlation filter classifiers , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[12]  Kim L. Boyer,et al.  Head pose estimation using view based eigenspaces , 2002, Object recognition supported by user interaction for service robots.

[13]  Timothy F. Cootes,et al.  View-based active appearance models , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[14]  Tieniu Tan,et al.  Head pose estimation using Gabor eigenspace modeling , 2002, Proceedings. International Conference on Image Processing.

[15]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[16]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Jieping Ye,et al.  A two-stage linear discriminant analysis via QR-decomposition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  D. Casasent,et al.  Minimum average correlation energy filters. , 1987, Applied optics.

[19]  Zhengyou Zhang,et al.  Robust part-based face matching with multiple templates , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[20]  Yan Yan,et al.  A Novel Unconstrained Correlation Filter and Its Application in Face Recognition , 2012, IScIDE.

[21]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[22]  Jie Chen,et al.  Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition , 2010, IEEE Transactions on Image Processing.

[23]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.