Your SlideShare is downloading. ×
face recognition using Principle Componet Analysis
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

face recognition using Principle Componet Analysis


Published on

Published in: Technology

1 Like
  • Be the first to comment

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 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.
  • 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.