pattern recognition

Amity School of Engineering & Technology

PATTERN RECOGNITION

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ABHIJITH MENONBALINI MANOJ KUMAR

SUDHANVI VELLALAMAAZ HASAN

PRIYANKA YADAV


CONTENTS

INTRODUCTION

PATTERN

PATTERN RECOGNITION

PATTERN RECOGNITION SYSTEM

PATTERN RECOGNITION MODEL

APPLICATION OF PATTERN RECOGNITION

CONCLUSION

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INTRODUCTION

Pattern Recognition is a branch of Artificial Intelligence.

Humans can recognize the faces without worrying about the varying illuminations. When implementing such recognition artificially ,it becomes a very complex task.

The field of Artificial Intelligence has made this complex task possible.


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PATTERN

A pattern is a set of objects or phenomena or concepts where the elements of the set are similar to one another in certain ways or aspects.

A pattern is an entity , that could be given a name .

Example : Fingerprint Image, handwritten word , human face , speech signal , DNA sequence etc.


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EXAMPLES


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PATTERN RECOGNITION

Pattern recognition is the procedure of processing and analizing diverse infornation ( numerical , literal, logical ) characterizing the objects or phenomenon , so as to provide descriptions ,identifications , classifications and interpretations for them .

“ Perceive + Process + Prediction ” – It is the study of how machine can

Perceive: Observe the environment (i.e. Interact with the real –world) .

Process: Learn to distinguish patterns of interest from their background.

Prediction: Make sound and reasonable decision s about the categories of the pattern.


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PATTERN RECOGNITION SYSTEM

Design model of a pattern recognition system essentially involves the following 4 steps:- Data acquisition and pre-processingData RepresentationFeature extractionDecision making


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PATTERN RECOGNITION PROCESS

Data acquisition and sensing:Measurements of physical variables.Important issues: bandwidth, resolution , etc.Pre-processing:Removal of noise in data.Isolation of patterns of interest from the

background.Feature extraction:Finding a new representation in terms of features.ClassificationUsing features and learned models to assign a

pattern to a category.Post-processingEvaluation of confidence in decisions.


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PATTERN RECOGNITION MODEL

Statistical model: Pattern recognition systems are based on statistics and probabilities.

Syntactic model: Structural models for pattern recognition and are based on the relation between features. Here the patterns are represented by structures .

Template matching model: In this model, a template or a prototype of the pattern to be recognized is available.

Neural network model: An artificial neural network (ANN) is a self-adaptive trainable process that is able to learn and resolve complex problems based on available knowledge.


PATTERN CLASS

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A Pattern class is a set of patterns sharing common attributes .

A collection of “Similar” ( not necessarily identical ) objects.

During recognition given objects are assigned to prescribed classes.


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CLASSIFICATION

SUPERVISED TRAINING/LEARNING:


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CLASSIFICATION

UNSUPERVISED TRAINING/LEARNING:


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CAD- Computer Aided Diaganosis

APPLICATIONS OF PATTERN RECOGNITION


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CAD- Computer Aided Design



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Pattern Recognition is used in any area of science and engineering that studies the structure of observations.

It is now frequently used in many applications in manufacturing industry, health care and military.


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Input: Images with characters (normally contaminated with noise)

Output: The identified character string

Useful in scenarios such as automatic license plate recognition (ALPR), optical character recognition(OCR) ,etc.

CHARACTER RECOGNITION


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Input: Documents , web pages, etc

Output: Category of the text , such as political , economic , military , sports etc

Useful in scenarios such as information retrieval , document organization, etc.

TEXT CHARACTERIZATION


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Input: Acoustic signal (Sound waves etc)

Output: Contents of the speech

Useful in scenarios such as speech-to-text (STT), voice command and control etc.

SPEECH RECOGNITION


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FINGERPRINT RECOGNITION Input: Fingerprint of some person

Output: The persons identity.

Useful in scenarios such as computerized access control , criminal pursuit, etc.


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Input: Signature of some person (Sequence of dots)

Output: The signatory’s identity

Useful in scenarios such as digital signature verification, credit card anti-fraud ,etc.

SIGNATURE RECOGNITION


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Input: Images with SEVERAL PEOPLE

Output: Locations of the peoples’ faces in the image.

FACE DETECTION


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• Used in the detection and diagnosis of Diseases.

• Electrocardiodiagram (ECG) waveforms are sent as input and types of cardiac condition and classes of brain condition is analysed accordingly.

• Is of great use to the paramedical industry.


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Brands use facing recognition to transform marketing.

facial recognition and simulation has been widely used for virtual makeovers and virtual product try-ons. Eg.VOGUE’s Makeup Simulation application, which recently launched in Japan.

facial detection and simulation is letting consumers interact with beauty products and brands on a more personal level.


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The impact of facial recognition and modeling on finance may not be very clear, and so far there are very few examples to show. One recent example that garnered significant media and customer interest was Merrill Edge’s Face Retirement application, which was created to entice customers to save for retirement.The basis of the app was a study from Stanford University that argued that if people were shown a photo of their older selves, they would be more likely to think about their retirement. As you can see in the photo above, Merrill Edge uses facial recognition and modeling to take a user’s photo to show them how they would look at 50, 60, 70, and all the way to 100.Although this is a relatively newer marketing campaign, early indications suggest it has been very successful in its quest to highlight the need to save for retirement.

http://faceretirement.merrilledge.com/?src_cd=SDPS1&cm_mmc=GWM-Edge-Int-_-Google-PS-_-merrill%20edge%20face%20retirement-_-Merrill%20Edge%20Exact


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Template matching is simple to implement but the template size must be small to decrease computational delay.

Statistical methods highly depends on the assumption of distribution.

Neural networks can adaptively refine the classifier and the decision surface in principle can be arbitrarily implemented .

Syntactic methods concerned structural sense to encode but additional process to define primitives are required.

CONCLUSION


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FUTURE WORKS

Frequency domain or Wavelet domain

Image compression method to face recognition

Video-based face recognition

Adding color factor into face recognition


28thank

you!

pattern recognition

Technology

pattern recognition

speech recognition

syntactic model

neural network model

speech useful

template matching model

set of objects

set of patterns