Pattern Recognition

Amity School of Engineering & Technology
PATTERN RECOGNITION
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ABHIJITH MENON
BALINI MANOJ KUMAR
SUDHANVI VELLALA
MAAZ 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-processing
Data Representation
Feature extraction
Decision 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.
Classification
Using features and learned models to assign a
pattern to a category.
Post-processing
Evaluation 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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APPLICATIONS OF PATTERN
RECOGNITION
• 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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RECOGNITION
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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RECOGNITION
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.

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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

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Pattern Recognition

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