Face recognition using laplacianfaces

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Face recognition using laplacianfaces

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Face recognition using laplacianfaces

  1. 1. Presented By: A.VAMSI KRISHNA B.PRAVEEN KUMAR N.V.PRUDHVI B.PRATHEEP KUMAR SK RASHID ALI Guided By: Mr. V.V Syam
  2. 2. • Introduction • History • Technology • Features • Limitations • Future Scope • References • Conclusion • Questions ?
  3. 3. Facial recognition systems are built on computer programs that analyze images of human faces for the purpose of Identifying them. The programs take a facial image, measure characteristics such as the distance between the eyes, the length of the nose, and the angle of the jaw, and create a unique file called a "template."
  4. 4. HISTORY
  5. 5. Perhaps the most famous early example of a face recognition system is due to Kohonen , who demonstrated that a simple neural net could perform face recognition for aligned and normalized face images. Kirby and Sirovich (1989) later introduced an algebraic manipulation which made it easy to directly calculate the eigenfaces, and showed that fewer than 100 were required to accurately code carefully aligned and normalized face images. Face Recognition using Elastic Graph Matching
  6. 6. Laplacianfaces refer to an appearance-based approach to human face representation and recognition. The approach uses Locality Preserving Projection (LPP) to learn a locality preserving subspace which seeks to capture the intrinsic geometry of the data and the local structure. When the projection is obtained, each face image in the image space is mapped to the low- dimensional face subspace, which is characterized by a set of feature images, they are called Laplacianfaces.
  7. 7. Principle Component Analysis(PCA) is an eigenvector method designed to model linear variation in high- dimensional data. Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. and Linear Discriminant Analysis (LDA) which effectively see only the Euclidean structure of face space, Two-dimensional linear embedding of face images by Laplacianfaces. As can be seen, the face images are divided into two parts, the faces with open mouth and the faces with closed mouth. Moreover, it can be clearly seen that the pose and expression of human faces change continuously and smoothly, from top to bottom, from left to right. The bottom images correspond to points along the right path (linked by solid line illustrating one particular mode of variability in pose.
  8. 8. FEATURESFEATURES
  9. 9.  Accuracy  Image Based Projection Techniques  KDT Algorithm  Face hallucination  Camera technology The Features Are:
  10. 10. Accuracy Consider numerous numbers of faces and from them it can select your face in any expression
  11. 11. 2. Image Based Projection Techniques Laplacian is based upon the processing of images. Input Image Matched Image Processing
  12. 12. KDT Algorithm The utilization of the KDT algorithm is quite effective in speeding up the kNN query process. By adopting the KDT method, the 2D Laplacianfaces is improved to be not only more efficient for training, but also as competitively fast as other methods for query and classification. 3D Tree
  13. 13. Face hallucination Face hallucination is super-resolution of face images, or clarifying the details of a face from a low-resolution image. The technique of sparse coding can be used. Because of the importance of face images in facial recognition systems and other applications, face hallucination has become an area of research.
  14. 14. Camera Technology Cameras can be used to detect the Faces and recognize a particular person "Camera technology designed to spot potential terrorists by their facial characteristics at airports failed its first major test at Boston's Logan Airport" To Search Someone
  15. 15. LIMITATIONS The human face has 80 nodal points, of which facial recognition software utilizes 14 to 22. Less accurate Only pgm file is used Does not deal with manifold structure It doest not deal with biometric characteristics
  16. 16. FUTURE SCOPES• A new dimension to facial recognition-3d • Unobtrusive audio-and-video based person identification systems. • Neven Vision, www.nevenvision.com a Santa Monica, Calif.-based developer of mobile machine vision technology. Neven Vision 3D Face Expression Unobtrusive
  17. 17. REFERENCES[1] A. U. Batur and M. H. Hayes, “Linear Subspace for Illumination Robust Face Recognition”, IEEE Int. Conf. on Computer Vision and Pattern Recognition, Hawaii, Dec. 11- 13, 2001. [2] P. N. Belhumeur, J. P. Hespanha and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, No. 7, 1997, pp. 711-720. [3] M. Belkin and P. Niyogi, “Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering”, Advances in Neural Information Processing System 15, Vancouver, British Columbia, Canada, 2001. [4] M. Belkin and P. Niyogi, “Using Manifold Structure for Partially Labeled Classification”, Advances in Neural Information Processing System 15, Vancouver, British Columbia, Canada, 2002. [ ]
  18. 18. Now We Know What is Face Recognition? Its History What Technology is used? What are its Features? Its limitations and Future?
  19. 19. QUESTIONSQUESTIONS

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