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Automated Face Detection and Recognition
 

Automated Face Detection and Recognition

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  • Full Name Full Name Comment goes here.
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  • sir can u send me ur code for this whole process on ravirkm007@gmail.com
    because i tried to make face recognition but the eigen faces are not coming.
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    Automated Face Detection and Recognition Automated Face Detection and Recognition Presentation Transcript

    • Automated Face Detection and Recognition A Survey Waldir Pimenta [email_address] Universidade do Minho Mestrado em Informática MI-STAR 2010
    • Face Detection Locating generic faces in images ©2009 Angelo State University
    • Face Detection: applications
        • Web cams that track the user
        • Cameras that shoot automatically when they detect smiles
        • Blurring of faces in public image databases
      ©2009 Google
        • Counting of people in a room (e.g. for temperature adjustment)
    • Face Recognition Distinguishing a specific face from other faces ©2009 TotallyLooksLike.com
    • Face Recognition: applications
        • Biometrics / access control
      ""Minority Report" ©2002 20th Century Fox Superbad" ©2007 Columbia Pictures
        • Searching mugshot databases
        • Tagging photo albums
        • Detecting fake ID cards
        •  
          • no action required
          • scan many people at once
          • places: airports, banks, safes
          • data: laptops, medical info
    • Humans vs. Computers
        • "Built-in" face detection / recognition ability
        • detection & recognition in different areas of the brain
        • can be fooled by look-alikes
      © SingularityHub.com
        • Algorithms must be built from scratch
        • Virtually perfect memory
        • Can work 24/7 without degrading performance
        •   Can apply stricter matching criteria
    • Computer representation of faces
        • Faces vary across many attributes — they're multidimensional
        • Plotted in spaces with more than 3 dimensions
          • in fact, it's commonly one dimension per pixel
          • on a 20×20px image, that's 400 dimensions!
        • Humans can't visualize or compute distances intuitively in >3D space. Computers can. But...
        • It is computationally intensive. Dimensionality reduction is applied to enhance efficiency
    • PCA: Principal component analysis
        • Data is projected into a lower dimensional space
        • preserving the directions that are most significant
        • not necessarily orthogonal to the original ones!
      cc-by  Lydia E. Kavraki <cnx.org/content/m11461/>
    • What defines a &quot;match&quot;?
        • Ideally, distance in &quot;facespace&quot; should be:
          • zero, for a specific match in face recognition
          • small, for a generic face
          • large, otherwise
        • But there are variations due to:
          • facial expressions
          • illumination variance
          • pose (orientation)
          • dimensionality reduction
    • The distance theshold
        • faces closer to each other than a given limit (threshold) are considered matches.
        •   A looser threshold can be used for face detection.
      ©  1991 M. Turk and A. Pentland
    • The ROC curve
        • Too low threshold = more false negatives
        • Too high threshold = more false positives
        • EER = Equal error rate
      © 2007 Y. Du and C.-I. Chang &quot;Handbook of Fingerprint Recognition&quot; © 2004 D. Maltoni et al.
    • Some history... Francis Galton (1888) Designed a biometric system for description and identification of faces © 2007 University of Texas at Austin Public Domain Woody Bledsoe (1964) First implementation of automatic facial recognition in a mug shot database.
        • Michael D. Kelly (1970)
          • Visual identification of people by computer
        • Takeo Kanade (1973)
          • Computer recognition of human faces
    • Classification Zhao et al. , 2003: “ [The facial recognition problem has] attracted researchers from very diverse backgrounds: psychology, pattern recognition, neural networks, computer vision, and computer graphics. ”   geometric (feature based) × photometric (image based) detection × recognition pre-processing 3D Video
    • Pre-processing
        • Face location / normalization
        • Later processing doesn't need to scan the whole image
        • Morphological operators (very fast)
        • Rough operators to detect heads
        • Finer confirmation operators to detect prominent features
      © Brunelli and Poggio 1993 © Reisfeld et al. , 1995
    • Eigenfaces
        • Sirovich and Kirby 1987; Turk and Pentland 1991
        • Uses PCA to discover principal components (eigenvectors)
        • Each face is described as a linear combination of the main eigenvectors
        • Image-based approach (features might not be intuitive)
        • eigenvectors can be translated back to the original pixel–based representation, many producing face-like images (hence the name eigenfaces )
      © AT&T Laboratories
    • Fisherfaces
        • Instead of PCA, it uses Linear disciminant analysis (LDA), developed by Robert Fisher in 1936
        • Variation can be greater due to lighting than due to different faces (Moses el al. 1994)
      ©1997 Belhumeur et al.
        • Shashua [1994] demonstrated that images from same face but under different illumination conditions lie close to each other in the high- dimensional facespace
        • LDA can grasp these similarities better than PCA, which makes Fisherfaces more illumination independent than eigenfaces
    • Neural networks
        • Based on the natural brain structure of simple, interconnected neurons
        • Good at approximating complex prob- lems without deterministic solutions
        • Each pixel of the face image is mapped to an input neuron
        • The intermediate (hidden-layer) neurons are as many as the number of reduced dimensions that are intended.
        • The network “learns” what patterns are likely faces or not
        • Initially promising, but Cottrell and Fleming [1990] showed that they can at best match an eigenface approach. 
      cc-by-sa Cburnett <commons.wikimedia.org>
    • Gabor wavelets
        • First proposed in 1968 by Dennis Gabor
        • Analog to Fourier series: images are decomposed in a series of wavelets applied in different points
        • Further developed to flexible models: elastic grid matching. 
      GFDL Wikimedia Commons © Wiskott et al. 1997
    • Active Shape/Appearance Models
        • Original concept by  Kass et al. , 1987: “snakes”, deformable curves that adjust to edges
        • Yuille [1987] extended the concept to flexible sets of geometrically related points (not necessarily on a curve)
        • Cootes [2001] applies statistical analysis to model and restrict the variation (flexibility) of model points
      ©2001 Cootes et al.
    • 3D
        • 2D deal poorly with varying poses (orientation) of the head
        • Many have attempted to compensate by storing several views per face
          • obviously resource-consuming
        •   3D attempts to solve this issue, using:
      ©2006 Bowyer et al.
        • active range sensors (laser scanners, ultrasound)
        • passive sensors (structured light: grid projected on face)
        • New poses can be matched by deforming the 3D model
    • Video
        • Lower quality images (frames), due to compression. Reconstructed models will have low accuracy.
        • Advantage: temporal coherence, optical flow
        • Simplest approach: use frame difference to detect moving foreground objects and match their shapes (blobs) to heads
        • Locate faces, then track them
        • Reconstruct 3D shape from relative movement of tracked points. This is called Structure from Motion (SfM)
      ©2010 Christian Rakete <http://www.dorfpunks.de>
    • Comparison
      • Standard tests needed for valid results comparison
      • Databases: FERET, MIT, Yale, and many smaller ones
      •  
      • Evaluations:
        • Face Recognition Vendor Test (FVRT)
        • Face Recognition Grand Challenge
        • XM2VTS
      • Conferences:
        • International Conference in Audio- and Video-Based Person Authentication (AVBPA)
        • International Conference in Automatic Face and Gesture Recognition (AFGR)
    • Questions?