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Face Recognition

Face Recognition. By Sunny Tang. Outline. Introduction Requirements Eigenface Fisherface Elastic bunch graph Comparison. Introduction. What is face recognition? Applications Security applications Image search engine. Requirements. Accurate Efficient Light invariant

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Face Recognition

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  1. Face Recognition By Sunny Tang

  2. Outline • Introduction • Requirements • Eigenface • Fisherface • Elastic bunch graph • Comparison

  3. Introduction • What is face recognition? • Applications • Security applications • Image search engine

  4. Requirements • Accurate • Efficient • Light invariant • Rotation invariant

  5. Eigenface • Euclidean distance between images • Principal component analysis (PCA) For training set T1, T2, …… TM Average face ψ = 1/MΣTM Difference vector φi = Ti –ψ Covariance matrix C = 1/MΣφn φTn

  6. PCA

  7. Recognition • Projection in Eigenface Projection ωi = W(T – ψ) W = {eigenvectors} • Compare projections

  8. Fisherface • Similar approach to Eigerface • Fisher’s Linear Discriminant (FLD) • PCA • Scatter Matrix • Projection Matrix

  9. Fisherface • FLD • Between-class scatter matrix • Within-class scatter matrix • Projection Matrix

  10. FLD

  11. Elastic Bunch Graph • Gabor wavelet decomposition • Gabor kernels

  12. Gabor Filters

  13. Jets • Small patch gray values • Wavelet transform

  14. Comparing Jets • Amplitude similarity • Phase similarity

  15. Comparing Jets

  16. Face Bunch Graphs (FBG) • Stack like general representation • Two types of FBG: • Normalization stage • Graph extraction stage • Graph similarity function

  17. Graph Extraction • Step 1: find approximate face position • Step 2: refine position and size • Step 3: refine size and find aspect ratio • Step 4: local distortion

  18. Recognition • Comparing image graph • Recognized for highest similarity

  19. Comparison • Eigenface • Fast, easy implementation • Fisherface • Light invariant, better classification • Elastic bunch graph • Rotation, light, scale invariant

  20. Q & A Section

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