Improving ATM security via face recognition

A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. Proposed paper uses face recognition technique for verification in ATM system. For face recognition, there are two types of comparisons. The first is verification, this is where the system compares the given individual with who that individual says they are and gives a yes or no decision. The next one is identification this is where the system compares the given individual to all the other individuals in the database and gives a ranked list of matches. Face recognition technology analyzes the unique shape, pattern and positioning of the facial features. Face recognition is very complex technology and is largely software based. This Biometric Methodology establishes the analysis framework with PCA algorithms for each type of biometric device. Face recognition starts with a picture, attempting to find a person in the image. This can be accomplished using several methods including movement, skin tones, or blurred human shapes.

[1]  Siome Goldenstein,et al.  The Best of Both Worlds: Combining 3D Deformable Models with Active Shape Models , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[2]  Takeo Kanade,et al.  3D Alignment of Face in a Single Image , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  Zhiwei Zhu,et al.  Robust Real-Time Face Pose and Facial Expression Recovery , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[6]  Xavier Pennec,et al.  Intrinsic Statistics on Riemannian Manifolds: Basic Tools for Geometric Measurements , 2006, Journal of Mathematical Imaging and Vision.

[7]  Ahmed M. Elgammal,et al.  Inferring 3D body pose from silhouettes using activity manifold learning , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[8]  Jing Xiao,et al.  Real-time combined 2D+3D active appearance models , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[9]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[10]  Shihong Lao,et al.  Boosting nested cascade detector for multi-view face detection , 2004, ICPR 2004.

[11]  Faune Hughes,et al.  Face Biometrics: A Longitudinal Study , 2009 .

[12]  Gérard G. Medioni,et al.  3D face tracking and expression inference from a 2D sequence using manifold learning , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Gérard G. Medioni,et al.  Tensor Voting: A Perceptual Organization Approach to Computer Vision and Machine Learning , 2006, Tensor Voting.

[14]  Wen Gao,et al.  Efficient 3D reconstruction for face recognition , 2005, Pattern Recognit..

[15]  Ron Kimmel,et al.  Texture Mapping Using Surface Flattening via Multidimensional Scaling , 2002, IEEE Trans. Vis. Comput. Graph..

[16]  Yoshua Bengio,et al.  Nonlocal Estimation of Manifold Structure , 2006, Neural Computation.

[17]  Gary G. Yen,et al.  Facial feature extraction using genetic algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[18]  Ahmed M. Elgammal,et al.  Learning to track: conceptual manifold map for closed-form tracking , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).