Computer Vision Pattern Recognition

Imaging for Forensics and Security: From Theory to Practice by Ahmed Bouridane

By Ahmed Bouridane

Imaging for Forensics and protection: From conception to Practice offers an in depth research of latest imaging and development acceptance thoughts for the knowledge and deployment of biometrics and forensic strategies. those recommendations can be utilized for useful suggestions to extend safeguard. the cloth features a choice of the hot advances within the know-how starting from idea, layout, and implementation to functionality overview of biometric and forensic structures. It additionally addresses new tools corresponding to the multiscale strategy, directional filter out financial institution, and wavelet maxima for the improvement of sensible recommendations to biometric problems.

The writer introduces a brand new forensic approach in line with shoeprint imagery with complex thoughts to be used in forensics purposes. the cloth additionally provides the idea that of defending the originality of biometric photographs saved in databases opposed to intentional and accidental assaults and fraud detection information as a way to additional elevate the security.

Imaging for Forensics and safeguard: From idea to Practice is an invaluable software for researchers and practitioners in imaging for defense and forensics.

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78%. This result clearly demonstrates the discriminating strength of a DFB pre-processing step. 1 0 30 45 60 Database Size 75 90 Fig. 4 LDA It is well known that the main problem with principal component methods (PCA and ICA) is the fact that they have no information about the class of each vector in the training database. This means that each face image is treated separately. This disadvantage has been resolved when using the LDA method since all the face images for one person are considered as one class.

99% for the PCA. 39%) for SDA. 83% has been obtained for the SDA algorithm combined with DFB pre-processing. References 1. P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min and W. Worek, “Overview of the face recognition grand challenge,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 947–954, June 2005. 2. A. Rosenfeld, W. Zhao, R. Chellappa and P. J. Phillips, “Face recognition: A literature survey,” ACM Computing Surveys, vol.

13, no. 6, pp. 1450–1464, November 2002. 5. P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman, “Eigenfaces vs. fisher-faces: Recognition using class specific linear projection,” IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711720, July 1997. 6. M. Zhu and A. M. Martinez, “Subclass discriminant analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1274–1286, August 2006. 46 3 Improving Face Recognition Using Directional Faces 7.

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