robust face

7/28/2019 Robust Face

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Robust Face-Name Graph Matching for Movie

Character Identification

AbstractAutomatic face identification of characters in movies has drawn significant research interests and

led to many interesting applications. It is a challenging problem due to the huge variation in the

appearance of each character. Although existing methods demonstrate promising results in clean

environment, the performances are limited in complex movie scenes due to the noises generated

during the face tracking and face clustering process. In this paper we present two schemes of

global face-name matching based framework for robust character identification. The

contributions of this work include: Complex character changes are handled by simultaneously

graph partition and graph matching. Beyond existing character identification approaches, we

further perform an in depth sensitivity analysis by introducing two types of simulated noises. The

proposed schemes demonstrate state-of-the-art performance on movie character identification in

various genres of movies.


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Literature Survey:

Layered Graph Matching by Composite Cluster Sampling with

Collaborative and Competitive Interactions

This paper studies a framework for matching an unknown number of corresponding structures in

two images (shapes), motivated by detecting objects in cluttered background and learning parts

from articulated motion. Due to the large distortion between shapes and ambiguity caused by

symmetric or cluttered structures, many inference algorithms often get stuck in local minimums

and converge slowly. We propose a composite cluster sampling algorithm with a candidacy

graph representation, where each vertex (candidate) is a possible match for a pair of source

and target primitives (local structure or small curves), and the layered matching is then

formulated as a multiple coloring problem. Each two vertices can be linked by either a

competitive edge or a collaborative edge. These edges indicate the connected vertices

should/shouldnt be assigned the same color. With this representation, the stochastic sampling

contains two steps: (i) Sampling the competitive and collaborative edges to form a composite

cluster, in which a few mutual-conflicting connected components are in different colors; (ii)

Sampling the new colors to this cluster remaining consistency with Markov Chain Monte Carlo

(MCMC) mechanism. The algorithm is applied to many applications on many public datasets

and outperform the state of the art approaches.

Clustering by Passing Messages Between Data Points

Clustering data by identifying a subset of representative examples is important for processing

sensory signals and detecting patterns in data. Such exemplars can be found by randomly

choosing an initial subset of data points and then iteratively refining it, but this works well only

if that initial choice is close to a good solution. We devised a method called affinity

propagation, which takes as input measures of similarity between pairs of data points. Real-

valued messages are exchanged between data points until a high-quality set of exemplars and

corresponding clusters gradually emerges. We used affinity propagation to cluster images of

faces, detect genes in microarray data, identify representative sentences in this manuscript, and

identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters


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with much lower error than other methods, and it did so in less than one-hundredth the amount of

time.

A new point matching algorithm for non-rigid registration

Feature-based methods for non-rigid registration frequently encounter the correspondence

problem. Regardless of whether points, lines, curves or surface parameterizations are used,

feature-based non-rigid matching requires us to automatically solve for correspondences between

two sets of features. In addition, there could be many features in either set that have no

counterparts in the other. This outlier rejection problem further complicates an already difficult

correspondence problem. We formulate feature-based non-rigid registration as a non-rigid point

matching problem. After a careful review of the problem and an in-depth examination of twotypes of methods previously designed for rigid robust point matching (RPM), we propose a new

general framework for non-rigid point matching. We consider it a general framework because it

does not depend on any particular form of spatial mapping. We have also developed an algorithm

|the TPS-RPM algorithm| with the thin-plate spine (TPS) as the parameterization of the non-rigid

spatial mapping and the soft assign for the correspondence. The performance of the TPS-RPM

algorithm is demonstrated and validated in a series of carefully designed synthetic experiments.

In each of these experiments, an empirical comparison with the popular iterated closest point

(ICP) algorithm is also provided. Finally, we apply the algorithm to the problem of non-rigid

registration of cortical anatomical structures which is required in brain mapping. While these

results are somewhat preliminary, they clearly demonstrate the applicability of our approach to

real world tasks involving feature-based non-rigid registration.


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Existing SystemIn this project is used to detect the face of movie characters and recognize the characters and the

existing system are taking the too much time to detect the face. But this one we can do it in a

minute process.

Disadvantages:

In the previous process the time taken for detecting face is too long in windows processed.

Proposed System

In this Robust Face-Name Graph Matching for Movie Character Identification is used to detect

the face of movie characters and the Proposed system is taking the minimum time to detect the

face. In this one we can do it in a minute process.

Advantages: In the proposed process the time taken for detecting face in minimum (min) time only in

windows processed.


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Modules

1. Design & Explain with Login

2. Detection

3. Recognition

Login

In this module is going to explain the Robust Face-Name Graph Matching for Movie Character

Identification designing and how we did the face detection and recognition in this project. The

images will explain about the facial fetching details. After that admin going to login with the

details which needed for the login page.

Detection

In this module we are going to detect the face of the movie characters. In this module we are

using the emgu cv library we must install the emgu cv library. After installing the emgu cv lib in

our project we need to add reference with the name emgu.cv, emgu.cv.util, emgu.cv.ui. When

you will complete the references you will get the emgu controls in the toolbox.

. Recognition

In this module we are going to recognize the face of the movie characters which is we previously

stored on the face database. We just found that the give the real name of it. This is going to be

done here. Here we are using the With the help of these eigenObjectRecognizer we are going to

recognize the face.

HARDWARE & SOFTWARE REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium IV 2.4 GHz.

Hard Disk : 40 GB.

Floppy Drive : 1.44 Mb.


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Monitor : 15 VGA Color.

Mouse : Logitech.

Ram : 512 MB.

SOFTWARE REQUIREMENTS:

Operating system : Windows XP Professional.

Coding Language : C#.NET

robust face

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