02-11-2016, 04:27 PM
1463637447-FaceRecognition1.ppt (Size: 598.5 KB / Downloads: 239)
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
4. 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.
FaceCam
Developed by VisionSphere.
Face recognition technology integrated with speech recognition in one device.
Features
User-friendly.
Cost-effective.
Non-intrusive.
Auto-enrollment Auto-location of user.
Voice prompting.
Immediate user feedback.
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