Face detection using Viola and Jones method and neural networks

Human face detection and recognition is a hot topic and an active area of research. It is common in several fields such as image processing and computer vision. It is the primary and the first step in wide range of applications such as face recognition, personal identification, identity verification, facial expression extraction, and gender classification [1]. In this paper, a multi stage model for face detection is integrated based on Viola and Jones algorithm, Gabor Filters, Principal Component Analysis, and Artificial Neural Networks (ANN). This model was trained and tested using CMU (Carnegie Mellon University) data set [2]. The model showed an enhanced performance in terms of face detection rate.

[1]  David M. Skapura,et al.  Neural networks - algorithms, applications, and programming techniques , 1991, Computation and neural systems series.

[2]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Bhaskar Gupta,et al.  Face Detection Using Gabor Feature Extraction and Artificial Neural Network , 2010 .

[4]  Hamid Dehghani,et al.  Face Detection using Gabor Wavelets and Neural Networks , 2008 .

[5]  Matti Pietikäinen,et al.  Face Recognition by Exploring Information Jointly in Space, Scale and Orientation , 2011, IEEE Transactions on Image Processing.

[6]  Qian Du,et al.  Hyperspectral Image Compression Using JPEG2000 and Principal Component Analysis , 2007, IEEE Geoscience and Remote Sensing Letters.

[7]  Ruslana Makovetsky,et al.  COMP-558 Fundamentals of Computer Vision Face Detection and Tracking with Web Camera , 2012 .

[8]  Shaoyi Du,et al.  Rotated Haar-Like Features for Face Detection with In-Plane Rotation , 2006, VSMM.

[9]  Wei-Lun Chao,et al.  Gabor wavelet transform and its application , 2010 .

[10]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[11]  I K Fodor,et al.  A Survey of Dimension Reduction Techniques , 2002 .

[12]  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.

[13]  M. Graña.  Face Recognition Algorithms , 2010 .

[14]  Oleg Seredin,et al.  Comparative Testing of Face Detection Algorithms , 2010, ICISP.

[15]  Nanda Kambhatla,et al.  Dimension Reduction by Local Principal Component Analysis , 1997, Neural Computation.

[16]  Y. Ben Jemaa,et al.  Automatic Gabor Features Extraction for Face Recognition using Neural Networks , 2008, 2008 First Workshops on Image Processing Theory, Tools and Applications.

[17]  B. Heisele.  Face Detection , 2001 .

[18]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[19]  Da'san,et al.  Multi-Stage Face DetectionSystem Using Viola & Jones Method and Neural Networks Based on Reduced Features from Gabor Wavelet Filters , 2013 .

[20]  Heather May,et al.  Wavelet-based Image Processing , 2015 .

[21]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[22]  James S. Walker.  Wavelet-based image processing , 2006 .

[23]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.