15-01-2013, 02:06 PM
Face Recognition
Face Recognition.ppt (Size: 598.5 KB / Downloads: 179)
Overview Of Face Recognition
Face Recognition Technology involves
Analyzing facial Characteristics
Storing features in a database
Using them to identify users
Facial Scan process flow :-
Sample Capture – sensors
Feature Extraction – creation of template
Template Comparison –
Verification - 1 to 1 comparison
- gives yes/no decision
Identification - 1 to many comparison
- gives ranked list of matches
Matching – Uses different matching algorithms
Technically a three-step procedure :-
Sensor –
takes observation.
develops biometric signature.
Eg. Camera.
Normalization –
same format as signature in database.
develops normalized signature.
Eg. Shape alignment, intensity correction
Matcher –
compares normalized signature with the set of normalized signature in system database.
gives similarity score or distance measure.
Eg. Bayesian technique for matching
Considerations for a potential Face Recognition System
Mode of operation
Size of database for identification or watch list
Demographics of anticipated users.
Lighting conditions.
System installed overtly or covertly
User behavior
How long since last image enrolled
Required throughput rate
Minimum accuracy requirements
Sensors
Used for image capture
Standard off-the-shelf PC cameras, webcams.
Requirements:
Sufficient processor speed (main factor)
Adequate Video card.
320 X 240 resolution.
3-5 frames per second.
( more frames per second and higher resolution lead to a better performance.)
One of the cheaper, inexpensive technologies starting at $ 50.
Components of FaceCam
Integrated Camera
LCD Display Panel
Alpha-Numeric keypad
Speaker, Microphone
Attached to Pentium II class IBM compatible PC (containing an NTSC capture card and VisionSphere’s face recognition software)
Advantages of FaceCam
Liveness test is performed.
False Accept rate and False Reject Rate is approximately 1%.
Other sensors
A4Vision technology-uses structured light in near-infrared range.
PaPeRo (NEC’s Partner-type Personal Robot)
Feature Extraction
Dimensionality Reduction Transforms
Karhunen-Loeve Transform/Expansion
Principal Component Analysis
Singular Value Decomposition
Linear Discriminant Analysis
Fisher Discriminant Analysis
Independent Discriminant analysis
Discrete Cosine transform
Gabor Wavelet
Spectrofaces
Fractal image coding
Principal Component Analysis
Each spectrum in the calibration set would have a different set of scaling constants for each variation since the concentrations of the constituents are all different. Therefore, the fraction of each "spectrum" that must be added to reconstruct the unknown data should be related to the concentration of the constituents
The "variation spectra" are often called eigenvectors (a.k.a., spectral loadings, loading vectors, principal components or factors), for the methods used to calculate them. The scaling constants used to reconstruct the spectra are generally known as scores. This method of breaking down a set spectroscopic data into its most basic variations is called Principal Components Analysis (PCA).
PCA breaks apart the spectral data into the most common spectral variations (factors, eigenvectors, loadings) and the corresponding scaling coefficients (scores).
Gabor Wavelet
The preprocessing of images by Gabor wavelets is chosen for its biological relevance and technical properties.
The Gabor wavelets are of similar shape as the receptive fields of simple cells in the primary visual cortex.
They are localized in both space and frequency domains and have the shape of plane waves restricted by a Gaussian envelope function.
Capture properties of spatial localization, orientation selectivity, spatial frequency selectivity and quadrature phase relationship.
A simple model for the responses of simple cells in the primary visual cortex.
It extracts edge and shape information.
It can represent face image in a very compact way.
SpectroFace
Face representation method using wavelet transform and Fourier Transform and has been proved to be invariant to translation, on-the-plane rotation and scale.
First order
Second order
The first order spectroface extracts features, which are translation invariant and insensitive to facial expressions, small occlusions and minor pose changes.
Second order spectroface extracts features that are invariant to on-the-plane rotation and scale.