FACE RECOGNITOIN TECHNIQUE

By: Suvigya Tripathi (09BEC094) Ankit V. Gupta (09BEC106)

Guided By: Prof. Bhupendra Fataniya

Dept. of Electronics and Communication Engineering,Nirma University at Ahmedabad.

REVIEW-1

What is face detection.The History.Challenges. 2D-Image Scan.Biometrics : Skin Texture Analysis.

Applications.

OUTLINE

Steps:

Face Detection: differentiate a human face from the background of the image or a real time video.

Feature Detection: record its features.

Face Recognition: Compare it to a data base.

INTRODUCTION

BLOCK DIAGRAM

Technique employed to distinguish a Human face from the rest of the background of the image.

WHAT IS FACE DETECTION

During 1964 and 1965, Bledsoe, along with Helen Chan and Charles Bisson , worked on using the computer to recognize human faces.

He was proud of this work, but because the funding was provided by an unnamed intelligence agency that did not allow much publicity, little of the work was published.

THE HISTORY

The first step for any automatic face recognition system system.

First step in many Human Computer Interaction systems.Expression RecognitionCognitive State/Emotional State Recognition

First step in many surveillance and security systems.

Video codingAutomatic Target Recognition(ATR)

IMPORTANCE OF FACE DETECTION

In – Plane RotationOut – Plane RotationLightingAging EffectsFacial ExpressionsFace Covered by long Hairs or Hand.

CHALLENGES

CHALLENGES

Different Approaches:◦Knowledge Based Approach◦Feature Invariant Method◦Template Matching Method

2D – IMAGE SCAN

It uses human-coded rules to model facial features, such as two symmetric eyes, a nose in the middle and a mouth underneath the nose.

KNOWLEDGE-BASED APPROACH

Pros: ◦Easy to come up with simple rules ◦Based on the coded rules, facial features in an

input image are extracted first, and face candidates are identified

◦Work well for face localization in uncluttered background

Cons:◦Difficult to translate human knowledge into

rules precisely: detailed rules fail to detect faces and general rules may find many false positives

◦Difficult to extend this approach to detect faces in different poses: implausible to enumerate all the possible cases

KNOWLEDGE-BASED APPROACH-SUMMARY

Feature invariant methods try to find facial features which are invariant to pose, lighting condition or rotation.

Skin colors, edges and shapes fall into this category.

FEATURE INVARIANT METHOD

FEATURE INVARIANT METHOD-NODAL POINT ANALYSIS

Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up the faceo Distance between the

eyeso Width of the noseo Depth of the eye socketso The shape of the

cheekboneso The length of the jaw line

Pros: ◦Features are invariant to pose and change in orientation.

Cons: ◦Difficult to locate facial features due to several corruption (illumination, noise, occlusion)

◦Difficult to detect features in complex background

FEATURE INVARIANT METHOD-SUMMARY

Template matching methods calculate the correlation between a test image and a pre-selected facial templates.

TEMPLATE MATCHING METHOD

Pros: ◦Simple

Cons: ◦Templates needs to be initialized near

the face images ◦Difficult to enumerate templates for

different poses (similar to knowledge-based methods)

TEMPLATE MATCHING METHOD-SUMMARY

Using skin color to find face segments is a vulnerable technique.

Non-animate objects with the same color as skin can be picked up since the technique uses color segmentation.

Then the face can be picked up using any of the approaches.

BIOMETRICSSKIN TEXTURE ANALYSIS

Lack of restriction to orientation or size of faces.

A good algorithm can handle complex backgrounds.

It is relatively insensitive to changes in expression, including blinking, frowning or smiling

Has the ability to compensate for mustache or beard growth and the appearance of eyeglasses.

SKIN TEXTURE ANALYSIS:ADVANTAGES

Security measure at ATM’s Digital Cameras Public Surveillance (CCTV’s) at

Airports, Hospitals, etc. Televisions and computers can

save energy by reducing the brightness.

APPLICATIONS

A set of two task:Face Identification: Given a face image that belongs to a person in a database, tell whose image it is.

Face Verification: Given a face image that might not belong to the database, verify whether it is from the person it is claimed to be in the database.

FACE RECOGNITION:OVERVIEW

Wikipedia.org How Stuff Works www.crazyengineers.com Kimmel, Ron.

"Three-dimensional face recognition". Retrieved 2005-01-01.

Ziyou Xiong, Univ. of Illinois at Urbana-Champaign

REFRENCE