BAYESIAN FACE RECOGNITIONUSING

MARKOV CHAIN MONTE CARLOMETHOD

ANAL T.MINJ BE/1114/06 AMIT RAJ LAKRA BE/152/05 SIDDHARTH G. MAJHI

BE/1148/06

GUIDE – PROF.ANNAPURNA S. M.

PROGRESS

Understanding of the related material is going on.

Implementing a PCA based face recognition algorithm using MATLAB as part of the learning process.

In the future , we have to work on implementing the MCMC algorithm for Bayesian theorem.

WHAT IS IMAGE PROCESSING?

A form of signal processing for which the input is an image such as a photograph or frames of video.

The output of image processing can be either an image or a set of characteristics or parameters related to an image.

Digital image processing -Use of computer algorithms to perform image processing on digital images.

DIP has many advantages over analog image processing :

-Allows a much wider range of algorithms to be applied to the input data

-Can avoid problems such as the build-up of noise and signal distortion during processing.

APPLICATIONS OF DIP

Applications-Classification-Feature extraction-Pattern recognition-Projection-Multi scale signal analysis

INTRO. TO FACE RECOGNITION

A face recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source.

Some face recognition algorithms identify faces by extracting landmarks or features from an image of the subject’s face.

Other algorithms normalize a gallery of face images and then compress the face data, only saving the data in the image which is useful for face detection.

TECHNIQUES USED FOR FACE RECOGNITION

Two main approaches of recognition algorithms

- Geometric : Looks at distinguishing features

-Photometric : Distills image into values and compares with templates

Popular recognition algorithms-Principal Component Analysis using eigenface-Linear Discriminate Analysis-Elastic Bunch Graph Matching-Hidden Markov Model-Dynamic Link Matching (neural networks)

IMAGE FROM VIDEO CAPTURE

IMAGE FROM VIDEO CAPTURE

DEFORMABLE TEMPLATE MATCHING

Deformable template matching is based on the Elastic Graph Bunching Matching Method.

The deformable templates used for the recognition process are constructed from face images of target individuals at multiple poses, labeled with feature point positions.

Each template consists of normalized feature point coordinates together with features computed by convolutions with Gabor wavelets at each of the feature points

We apply templates to input video frames and deform them by shifting the feature points so as to maximize the similarity to the Gabor features in the template.

We then compute an overall match score for each deformed template, incorporating a penalty related to the deformation

BAYESIAN APPROACH

In general, finding the global maximum of the similarity function is difficult, and prone to falling into local maxima. Moreover, the optimal value of each parameter often depends on the other variable parameters and fixed system parameters, and good results may be achieved only if all parameters are optimized with respect to the input data. But generalizing the optimization to unknown input data may not be feasible without some strong constraints or assumptions on the data. A Bayesian approach, however, offers a principled way of tackling this problem.

Within a Bayesian framework , the feature familiarity term can be used to define a likelihood function:

BAYESIAN APPROACH

Bayes formula gives the joint posterior distribution of x, α, and β given the observed data D :

Now let Dtrain denote the training data and d the test data

BAYESIAN APPROACH (CONTINUED)

MARKOV CHAIN MONTE CARLO METHODS

o Markov chain Monte Carlo (MCMC) methods are a class of algorithms used for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium distribution.

o The state of the chain after a large number of steps is then used as a sample from the desired distribution.

o The quality of the sample improves as a function of the number of steps.

MCMC SAMPLER FOR BAYESIAN FARMEWORK

The most common application of these algorithms is numerically calculating multi-dimensional integrals.

Continuing the Bayesian approach , we apply the MCMC sampler to the complex multidimensional integral obtained previously.

So , we finally obtain :

DISCUSSION OF THE EXPERIMENTAL RESULTS

The Bayesian MCMC approach was able to recognize all the faces in the neutral test data without error.

-On the talking test data, a small sample of which was used for training, the

proposed approach reduced the ID error rate from 8.2% to 0.1%, showing that the MCMC samples adequately represent likely patterns of facial deformation during speech.

-On the smiling test data, none of which was used for training, the Bayesian MCMC approach reduced the ID error rate by over 90%

FUTURE WORK The presently available prototype uses

the Markov Chain Monte Carlo method under the Bayesian framework in order to calculate the posterior probabilities.

Scope of improvement Sequential Monte Carlo(SMC) may be effective

in representing the importance of past data. This may further reduce the error rate in

identification observed till now.

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