A face detection and location method based on Feature Binding

A face detection and location method based on Feature Binding (FB) is proposed in this paper. The features used for face detection and location are classified and bound into groups. The information of each group is extracted separately during face detection. Through the combination with the constraint relationship, the precise location of the face in the image could be identified by confidence coefficients of all groups. Experimental results show that this proposed method can improve the accuracy rate obviously and has good detection effect on obscured faces. Besides, FB can be good to adapt to varieties of features. Feature Binding (FB) in the field of pattern recognition is proposed.FB increases accuracy of face detection.FB is effective for the obscured face.FB has a good adaptability for varieties of features.

[1]  A Treisman,et al.  Feature binding, attention and object perception. , 1998, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[2]  Yuanqing Wang,et al.  Multi-User Autostereoscopic 2D/3D Switchable Flat-Panel Display , 2014, Journal of Display Technology.

[3]  Timothy F. Cootes,et al.  Diffeomorphic Statistical Shape Models , 2008, BMVC.

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

[5]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[6]  Erik Learned-Miller,et al.  FDDB: A benchmark for face detection in unconstrained settings , 2010 .

[7]  Zhengyou Zhang,et al.  Improving multiview face detection with multi-task deep convolutional neural networks , 2014, IEEE Winter Conference on Applications of Computer Vision.

[8]  Xiujie Zhao,et al.  Pupil Localization for Multi-View Eyeballs by ASM and Eye Gray Distribution , 2011 .

[9]  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.

[10]  Kongqiao Wang,et al.  A hierarchical multiscale and multiangle system for human face detection in a complex background using gravity-center template , 1999, Pattern Recognit..

[11]  Zhang Pin-zheng.  Face Detection and Validation Method in Complex Background , 2004 .

[12]  Jun-Di Ding,et al.  To Evaluate Salience Map towards Popping out Visual Objects: To Evaluate Salience Map towards Popping out Visual Objects , 2012 .

[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]  A. Khatri,et al.  Comparative Analysis of Offline Handwriting Recognition Using Invariant Moments with HMM and Combined SVM-HMM Classifier , 2013, 2013 International Conference on Communication Systems and Network Technologies.

[15]  Xiaogang Wang,et al.  Random Sampling for Subspace Face Recognition , 2006, International Journal of Computer Vision.

[16]  Thomas S. Huang,et al.  Human face detection in a complex background , 1994, Pattern Recognit..

[17]  Lorenzo Livi,et al.  Optimized dissimilarity space embedding for labeled graphs , 2014, Inf. Sci..

[18]  Zhang Changshui.  Regional feature based fast human face detection , 1999 .

[19]  B. Hommel.  Event files: feature binding in and across perception and action , 2004, Trends in Cognitive Sciences.

[20]  Stan Z. Li,et al.  Regularized Transfer Boosting for Face Detection Across Spectrum , 2012, IEEE Signal Processing Letters.

[21]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[22]  Ma Ru,et al.  To Evaluate Salience Map towards Popping out Visual Objects , 2012 .

[23]  耿庆田 Geng Qing-tian,et al.  Adaptive segmentation for visual salient object , 2013 .

[24]  Wang Yuan-qing.  Face detection method for real-time anti-interference , 2013 .

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

[26]  Poonam Sharma,et al.  Efficient face recognition using wavelet-based generalized neural network , 2013, Signal Process..

[27]  Nicolas D. Georganas,et al.  Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques , 2011, IEEE Transactions on Instrumentation and Measurement.

[28]  V. Lollo.  The feature-binding problem is an ill-posed problem , 2012, Trends in Cognitive Sciences.

[29]  Takeo Kanade,et al.  Neural network-based face detection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Chun-Hsian Huang.  A reconfigurable point target detection system based on morphological clutter elimination , 2014, J. Syst. Archit..

[31]  Bijaya K. Panigrahi,et al.  Streamflow forecasting by SVM with quantum behaved particle swarm optimization , 2013, Neurocomputing.

[32]  Chao Yan,et al.  Robust real-time multi-user pupil detection and tracking under various illumination and large-scale head motion , 2011, Comput. Vis. Image Underst..

[33]  Alessandro Sperduti,et al.  Speed up learning and network optimization with extended back propagation , 1993, Neural Networks.