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A Markov Random Field Groupwise Registration
Framework for Face Recognition
Abstract
We have implemented an efficient system to recognize faces from images with
some near real-time variations. The face recognition cycle starts with the capturing
of an individual’s face (normally either by still cameras, surveillance cameras or
web cameras captured pictures). After wards the captured image is manipulated
using a combination of algorithms and neural network technology to match face
prints against others stored in a populated back-end database. As comparisons are
made, the system assigns a value to the comparison using a predetermined
threshold, a match is declared. This form of face recognition is used today to make
accurate facial matches. Currently, this technology is being implemented in parks,
airports, arenas and other popular tourist locations.
In this paper, we propose a linear approach, called pixel Matching, for face
recognition. This system “Eigen faces” takes eight points on the face and performs
some algebraic manipulations to secure a match. Neural Networks is used to
increase reliability and offer a accurate match. Another form of facial recognition

2. is survival exponential entropy which uses graphic algorithms to trace points for
object recognition.
Existing System
 Face is a complex multidimensional visual model and developing a
computational model for face recognition is difficult.
 Facial recognition systems are computer-based security systems that are able
to automatically detect and identify human faces.
 These systems depend on a recognition algorithm. Principal Component
Analysis (PCA) is a statistical method under the broad title of factor
analysis.
 The Trace transform is a very rich representation of an image and in order to
use it directly for recognition, one has to produce a much simplified version
of it.
 This will not yield accurate recognition system.
 Less accurate
 It does not deal with biometric characteristics.

3. Proposed System
 In this paper, we propose a linear approach, called pixel Matching, for face
recognition.
 Another form of facial recognition is survival exponential entropy matching
which uses graphic algorithms to trace points for object recognition.
 The transformed probe image is then matched with the gallery images which
display neutral expression.
 Neural Networks is used to increase reliability and offer a accurate match
are used as inputs for positive and negative values variations.
 New test image is taken for recognition (from test dataset) and its face
descriptor is calculated from the Eigen faces found before.

4. SOFTWARE REQUIREMENTS
 Platform : JAVA (JDK 1.5)
 Front End : JAVA Swing
 Back End : MySql
 IDE : NETBEANS 6.9
 Operating System : Microsoft Windows 2000 or XP
HARDWARE REQUIREMENTS
 Processor : Pentium IV Processor
 RAM : 512 MB
 Monitor : 14” VGA COLOR MONITOR
 Keyboard : 104 Keys
 Capturing Device : Web cam