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face recognition using Principle Componet Analysis

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  • 1. Face recognition using principal component analysis by ABHILASH KOTAWAR VENKATA NARAYANA CHETTELA KOMIRISHETTI SRAVAN
  • 2.  In todays networked world, the need to maintain the security of information is becoming both increasingly important and increasingly difficult. BIOMETRICS represents a good compromise between what’s socially acceptable and what’s reliable, even when operating under controlled conditions. Recently, technology became available to allow verification of "true" individual identity. This technology is based in a field called "biometrics".
  • 3.  Face Recognition is the process of identification of a person by their facial image. This technique makes it possible to use the facial images of a person to authenticate him into a secure system, for criminal identification, for passport verification,... Face recognition technology is the least intrusive and fastest biometric technology. Face recognition systems unobtrusively take pictures of peoples faces as they enter a defined area. This method is found to be fast, relatively simple, and works well in a constrained environment.
  • 4.  PCA is a dimensionality reduction technique based on extracting the desired number of principal components of the multi-dimensional data. PCA aims to: Summerise data with many independent variables to a smaller set of derived variables. identifying patterns in data, and expressing the data in such a way as to highlight their similarities and differences.
  • 5. Get some data: x y 1.4000 1.6500 1.6000 1.9750 -1.4000 -1.7750 Mean=∑ Xi/n -2.0000 -2.5250 -3.0000 -3.9500 2.4000 3.0750 variance=(∑(xi-avg)²)*1/(n-1) 1.5000 2.0250 2.3000 2.7500 sum of variances=16.3756 -3.2000 -4.0500 -4.1000 -4.8500Average -0.4500 -0.5675Variance 6.4228 9.9528
  • 6.  For covariance we will use function (∑(x-xbar)*(y-ybar)/(n-1) X-Xbar Y-Ybar (X-Xbar)*(Y-Ybar) 1.8500 2.2175 4.1024 2.0500 2.5425 5.2121-0.9500 -1.2075 1.1471-1.5500 -1.9575 3.0341-2.5500 -3.3825 8.6254 2.8500 3.6425 10.3811 1.9500 2.5925 5.0554 2.7500 3.3175 9.1231-2.7500 -3.4825 9.5769-3.6500 -3.4825 15.6311 7.9876 covariance
  • 7.  In general the covariance matrix is = [covariance(x,x) covariance(x,y) covariance(y,x) covariance(y,y)] = [variance(x) covariance(x,y) covariance(x,y) variance(y)] = [6.4228 7.9876 7.9876 9.9528] To obtain Eigen values by solving function determinant {A-lamda(I)}=0 Solving equation A, we get the Eigen values are lamda=16.36809984,0.007462657 Here sum of two eigen values is always equal to the sum of variances
  • 8.  To obtain Eigen vector by solving for matrix x in such a way that, {A-lambda(i)}*[X]=[0]. For first Eigen value 16.36809984, we get [X]=[0.6262 0.7797] For second Eigen value 0.007462657,we get [X]=[0.7797 -0.6262] To obtain coordinates of data point in the direction of Eigen vectors by multiplying the centered data matrix to the Eigen vector matrix
  • 9. Projection on Projection on the line of the line of first principal second component principal componentX-Xbar Y-Ybar 2.88737 0.505380 1.8500 2.2175 3.26600 0.00622 2.0500 2.5425 -1.53633 0.01545-0.9500 -1.2075 -2.49680 0.01729-1.5500 -1.9575 -4.23402 0.12995-2.5500 -3.3825 4.62439 0.05886 2.8500 3.6325 3.24237 0.10306 1.9500 2.5925 2.7500 3.3175 4.30858 0.06669-2.7500 -3.4825 -4.43722 0.03664-3.6500 -4.2825 -5.62453 0.16411 16.36809775 0.007462657
  • 10. STEP1.Get some dataSTEP2.subtract the meanSTEP3.Calculate the covariance matrixSTEP4.Calculate the Eigen vectors & Eigen values of the covariance matrixSTEP5. choosing components and forming a feature vector The variance of projections in the line of principal component is equal to the Eigen values of the principal components. First Eigen vector is able to explain around 99% of total variance
  • 11.  DATABASE PREPATATION TRAINING TESTINGFlow chart indicating thesequence of implementation
  • 12. 1.Acess control ATM AIRPORT A door lock control system 2.Entertainment: Video Game Human Computer Interaction Human Robotics
  • 13. 3 Smart cards: Driver’s license Passports Voter registrations Pan card4 Information Security: Desktop Logon Personal Driven Logon Database security5 law Enforcement And Surveillance: Advanced video surveillance Drug trafficking  And some other Commercial Applications:
  • 14. HARD TO FOOL Face recognition is also very difficult to fool. It works by comparing facial and marks - specific proportions and angles of defined facial features - which cannot easily be concealed by beards, makeup. Byusing the facial recognition software, theres no need for a picture ID, bankcard or personal identification number (PIN) to verify a customers identity. This way business can prevent fraud from occurring.
  • 15. A face needs to be well lighted by controlled light sources in automated face authentication systems. This is only a first challenge in a long list of technical challenges that are associated with robust face authentication.The risk involved with identity theft.
  • 16.  Face recognition is a both challenging and important recognition technique. Among all the biometric techniques, face recognition approach possesses one great advantage, which is its user-friendliness. Face recognition promises latest security invents in the upcoming trends based on bio- metrics and pattern matching techniques and algorithms.
  • 17. CONCLUSION:
  • 18.  The image may not always be identified in facial recognition alone. A picture is taken of a patch of skin, & is then broken up into smaller blocks, Using algorithms.  It can identify differences between identical twins, which is not yet possible using facial recognition software. Accurate identification can increase by 20 to 25 percent.