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Deploying face recognition system with neural network & sub-space techniques

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  1. Abstract
  2. Introduction
  3. FRS classification based on face images
    1. Face recognition techniques (FRT)
  4. Face recognition systems structure
  5. Implementation of principal component analysis
  6. Artificial neural network (ANN)
    1. ANN based classification and network evolution
  7. Back propagation algorithms
    1. Initialization of weights
    2. Choice of learning rate (H)
    3. Momentum constant (MC)
  8. Algorithm
  9. Method
  10. Result and conclusion
  11. References

Now-a-days Human-Computer interaction involves face recognition with high recognition efficiency. Face recognition mainly includes signal processing, face tracking, pose estimation and expressions recognition. The face images are transformed into face spaces by a set of Eigen faces efficiently representing its projection onto the spaces. The Principle Component analysis is carried out on the Eigen values and is mapped as input to the feed forward neural network. The feed forward neural network is trained by the Back propagation algorithm. The learning technique is proved for their correctness. The paper emphasizes to implement the neural network resulting into high efficiency. The technique is evaluated on the Olivetti Research Laboratory (ORL) in Cambridge, England, and University of Manchester Institute of Science and Technology (UMIST) face database achieving the recognition efficiency 96.83%.

[...] Hidden Layer 2 Figure 4 Neural Network 6.2 CHOICE OF LEARNING RATE Weight vector changes in back propagation are proportional to the negative gradient of the error. It determines the relative changes that must occur in different weights when a training sample (or a set of samples) is presented, but does not fix the exact magnitudes of the desired weight changes. The magnitude change depends on the appropriate choice of the learning rate ?. A large value of ? will lead to rapid learning but the weight may then oscillate, while low value simply slow learning. [...]

[...] RESULT & CONCLUSION USING ORL DATABASE Table 1 shows the recognition efficiency achieved by using the feedforward neural network with the parameters selected lr= e=9000, goal is set between 1]. and number of hidden units using ORL database. Table 1. Recognition efficiency achieved using ORL Face No. of Images No. of Hidden layers Table 2 shows the recognition efficiency achieved if the goal is set between [ ] using ORL database. Table 2 Recognition efficiency achieved using ORL Face No. [...]

[...] Information Forensics and Security, IEEE Transactions on , Dec Volume:3,Issue:4,On page(s): 734-748 Yuxio Hu, Thomas S.Huang ?Subspace Learning For Human Head Pose Estimation?, Multimedia and Expo IEEE Conference on June 23 -April page(s): 1585-1588 F.K.Rama Linga Reddy,Prof G,R Babu,Prof. Lal Kishore,ETM, GNITS, Hyderabad, ?Multi Scale Feature And Single Neural Network Based Face Recognition?, IJCSNS International Journal Computer Science and Network Security, vol-8, No.-6, June 2008 M.savvides, R.Abiantun , J.Heo, S.Park, C.Xi and B.V.Vijayakumar ?Partial & Hoslistic Recognition on FRGC data using support vector machine. Computer Vision and Pattern Recognition Workshop CVPRW apos;06. Vol, Issue , 17-22 June 2006 Page(s): 48 48 Kiminori Sato, Shishir Shah, J.K. Aggarwal Partial Face using [...]

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