Real time smart car lock security system using face detection and recognition

An improved face detection and recognition method based on information of skin color is proposed in this paper. Color is a powerful fundamental cue of human faces. Skin color detection is first performed on the input color image to reduce the computational complexity. Morphological operations are used and it gives a prior knowledge for face detection. Face is detected by Adaboost algorithm. AdaBoost learning is used to choose a small number of weak classifiers and to combine them into a strong classifier deciding whether an image is a face or not. Then, by using principal component analysis(PCA) algorithm, a specific face can be recognized by comparing the principal components of the current face to those of the known individuals in a facial database built in advance.

[1]  Hans-Peter Seidel,et al.  Rapid stereo-vision enhanced face detection , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[2]  Qiuqi Ruan,et al.  Face stereo matching and disparity calculation in binocular vision system , 2010, 2010 2nd International Conference on Industrial and Information Systems.

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  Hans-Peter Seidel,et al.  Accurate Real-Time Disparity Estimation with Variational Methods , 2009, ISVC.

[5]  Stan Z. Li,et al.  Learning to detect multi-view faces in real-time , 2002, Proceedings 2nd International Conference on Development and Learning. ICDL 2002.

[6]  JIAN-GANG WANG,et al.  Real-time Stereo Face Recognition by Fusing Appearance and Depth Fisherfaces , 2007, J. VLSI Signal Process..

[7]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[8]  Liang Jimin.  Variant Pose Face Detection Based on Multi-classifier Fusion , 2009 .

[9]  Andrea F. Abate,et al.  2D and 3D face recognition: A survey , 2007, Pattern Recognit. Lett..

[10]  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.

[11]  Alexander M. Bronstein,et al.  Three-Dimensional Face Recognition , 2005, International Journal of Computer Vision.

[12]  Michael G. Strintzis,et al.  Use of depth and colour eigenfaces for face recognition , 2003, Pattern Recognit. Lett..

[13]  Patrick J. Flynn,et al.  A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition , 2006, Comput. Vis. Image Underst..

[14]  Brendan J. Frey,et al.  Mixtures of local linear subspaces for face recognition , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[15]  Michael G. Strintzis,et al.  Three Dimensional Face Recognition , 2008, Encyclopedia of Multimedia.

[16]  Rainer Lienhart,et al.  Empirical Analysis of Detection Cascades of Boosted Classifiers for Rapid Object Detection , 2003, DAGM-Symposium.

[17]  Te-Hsiu Sun,et al.  Face recognition using 2D and disparity eigenface , 2007, Expert Syst. Appl..