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PATTERN RECOGNITIONRecognize pattern and face

Presented by : Anup, Randhir and Shailesh

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Intro

Identify from the knowledge of characteristics or appearance by determining different aspects of face.

Measures physiological characteristics of a part of human body known as face to verify and identify its to previous similar pattern from data sources.

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Abstract

Humans detect and identify faces in a scene with little or no effort. We present a system for recognizing human faces from single images out of a large database containing one image per person. However, building an automated system that accomplishes this task is very difficult. There are several related sub-problems: 

detection of a pattern as a face, 

identification of the face, 

analysis of facial expressions, and 

classification based on physical features of the face.

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Importance

A system that performs these operations will find many applications,

Facebook includes facial recognition system.

criminal identification,

authentication in secure systems, etc.

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Process• Capture-A physical or behavioural sample is captured by the system during enrolment and also in

identification or verification process.

• Extraction- Unique data is extracted from the sample.

• Comparison- Compared with a new sample.

• Match/ non match- The system decides if the features extracted from the new samples are equivalent or not. It starts with a picture, attempting to find a person in the image. Mark the head and eye position. A matrix is then developed based on the characteristics of the individual face (eye, mouth, nostrils).

Capture Extraction Comparison

Match/Not Match

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Components of Facial Recognition

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Implementation of Face Recognition System

Face Image Data acquisition and Database Creation

Input Processing

Face image classification and decision making

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Face Image Data Acquisition and Database Creation

Scan face from some static camera or video system that generates the high resolution images

High quality enrollment is required to eventual identification and verification enrollment images define facial characteristics to be used in future authentication events.

A test set was created by taking images of the six people in the database.

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Input Processing

A pre-processing module marks the eye position and also looks after the surrounding lighting condition and colour variance. After the face is detection, localization and normalization are carried out. The appearance of the face can change considerably during speech and due to facial expressions. Some facial recognition approaches use the whole face while others concentrate on facial components and/ or regions such as:

• distance between eyes and depth of it• lips• nose• cheeks• jaw line• chin

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Face Image Classification and Decision Making

Synergetic computer are used to classify optical and audio features, respectively. A synergetic computer is a set of algorithm that simulate synergetic phenomena. In training phase the BIOID creates a prototype called face print for each person. A newly recorded pattern is pre-processed and compared with each face print stored in the database. As comparisons are made, the system assigns a value to the comparison using a scale of one to ten. If a score is above a predetermined threshold, a match is declared.

How Face Recognition System works?

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Face recognition system work by a particular software. There are about 80 nodal points on a human face. Here are few nodal points that are measured by the software: Distance between the eyes Width of the nose Depth of the eye socket Cheekbones Jaw line ChinThese nodal points are measured to create a numerical code, a string of numbers that represent a face in the database. This code is called face print. Only 14 to 22 nodal points are needed for detecting face and complete the recognition process. Nodal Point Alignment Normalization Representation Matching

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Elastic Bunch Graph Matching

This method generate initial graphs for the system, one graph for each pose, together with pointers to indicate which pairs of nodes in graphs for different poses correspond to each other. Once the system has an FBG (possibly consisting of only one manually defined model), graphs for new images can be generated automatically by Elastic Bunch Graph Matching. The matching procedure are as follows:

Find approximate face position

Refine position and size

Refine size and find aspect ratio

Refine size and find aspect ratio Grids for face findings Grids for face recognition

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ADVANTAGES AND DISADVANTAGES

Advantages

• There are many benefits to face recognition systems such as its convenience and Social acceptability. all you need is your picture taken for it to work.

• Face recognition is easy to use and in many cases it can be performed without a Person even knowing.

• Face recognition is also one of the most inexpensive biometric in the market and Its price should continue to go down.

Disadvantages

• Face recognition systems can’t tell the difference between identical twins.

Conclusion

Face recognition methods have been related with very expensive secure applications. Some applications of face recognition technology are economical, reliable and highly accurate. So there is no technological or financial obstacle to move to widespread deployment.

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THANK YOU