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Face Recognition using Parallel Associative Memory

Face Recognition using Parallel Associative Memory

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Face Recognition using Parallel Associative Memory

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  1. Face Recognition using Parallel Associative Memory 2009/12/31 學生:羅國育

  2. Outline • Introduction • Auto-associative memory for face recognition • Parallel associative memory for face recognition • Face similarity measure for recognition • Experimental Results • Conclusion

  3. Introduction • This paper proposes parallelization of the auto-associative memory in order to apply it for recognition of high resolution face images. • The Olivetti Research Laboratory (ORL) face database.

  4. Auto-associative memory for face recognition (1/3) • Associative memories mimic the capacity of human brain to recall information in a robust and associative access mode. • Neural network.

  5. Auto-associative memory for face recognition (2/3) • The input-output relationship in a linear associative memory is described by M × N gray scale images. W is an (MN)×(MN) matrix. (1) (2)

  6. Auto-associative memory for face recognition (3/3) • Now substituting W from (2) we have (3)

  7. Parallel associative memory for face recognition (1/4) • The system mainly performs three tasks: (i) Information storage, (ii) information retrieval based on some input pattern and (iii) matching of the information.

  8. Parallel associative memory for face recognition (2/4)

  9. Parallel associative memory for face recognition (3/4)

  10. Parallel associative memory for face recognition (4/4)

  11. Face similarity measure for recognition (1/2)

  12. Face similarity measure for recognition (2/2) • Run length: 兩個1之間,0出現的次數分別是 3、9、3、2 的話,這些值就稱為0的 run length。 Fig. 2. A grid structure to explain the concept of run length count

  13. Experimental Results (1/4) • The ORL database contains total 400 images of 40 individuals from various ethnicity and sex under various pose, light, scale and expression. • One set is considered as training set and the remaining 39 sets are used as testing set.

  14. Experimental Results (2/4) • In this experiment values of N, n and Tr are set to 128, 16 and 6 respectively. • It is found that the best suitable value of Th1 and Th2 are 40 (i.e, 65 % portion of input image) and 25 (i.e, 40 % portion of input image) respectively.

  15. Experimental Results (3/4)

  16. Experimental Results (4/4) • False Rejection Ratio (FRR) is 1% and 5.2% respectively. • For false accept experiments the system was tested with 40randomly chosen face images which are not present in the database.The observed FAR is 3.1%.

  17. Conclusion • In this paper a parallel associative memory based efficient face recognition system has been proposed. • The goal is to scale the associative memories to high resolution images. • A novel run length count based measure of face similarity suited for parallel associative memory is also proposed.

  18. Thanks for your attention! 2014/11/21