Face Reco.ppt

7/29/2019 Face Reco.ppt

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aceRecognition

Presented By:-

Yogen Sharma

1509262

EC-6



Contents

s Face Segmentation/Detection

s Facial Feature extraction

s

Face Recognitions Video-based Face Recognition

s Comparison



aceSegmentation/Detection

Before the middle 90’s, the research

attention was only focused on single-

face segmentation. The approaches

included:x Deformable feature-based template

x Neural network

x Using skin color



FaceSegmentation/Detecti

on During the past ten years, considerable

progress has been made in multi-face

recognition area, includes:x Example-based learning approach by Sung

and Poggio (1994).

x The neural network approach by Rowley et

al. (1998).

x Support vector machine (SVM) by Osuna et

al. (1997).



Example-based learningapproach (EBL)

Three parts:s The image is divided into many possible-

overlapping windows, each window

pattern gets classified as either “a face”

or “not a face” based on a set of localimage measurements.

s For each new pattern to be classified,

the system computes a set of different

measurements between the new patternand the canonical face model.

s A trained classifier identifies the new

pattern as “a face” or “not a face”.



Example of a systemusing EBL



Neural network (NN)

s Kanade et al. first proposed an NN-

based approach in 1996.

s Although NN have received significant

attention in many research areas, fewapplications were successful.

Why?



Neural network (NN)

s It’s easy to train a neural network with

samples which contain faces, but it is

much harder to train a neural network

with samples which do not.s The number of “non-face” simples are

just too large.



Neural network (NN)

s Neural network-based filter. Asmall filter window is used to scan

through all portions of the image,

and to detect whether a face exists

in each window.

s Merging overlapping detections

and arbitration. By setting a small

threshold, many false detectionscan be eliminated.



Test results of using NN



SVM

s SVM was first proposed in 1997, it

can be viewed as a way to train

polynomial neural network or radialbasic function classifiers.

s Can improve the accuracy and

reduce the computation.



Comparison withEBL

s Test results reported in 1997.

s Using two test sets (155 faces).

SVM achieved better detection rateand fewer false alarms.



Recentapproaches

Face segmentation/detection areastill remain active, for example:x An integrated SVM approach to multi-

face detection and recognition was

proposed in 2000.x A technique of background learning was

proposed in August 2002.

Still lots of potential!



Video-based FaceRecognition

s Three challenges:x Low quality

x Small images

x Characteristics of face/human objects.

s Three advantage:x Allows Provide much more information.

x Tracking of face image.

x Provides continuity, this allows reuse of

classification information from high-qualityimages in processing low-quality images from

a video sequence.



Basic steps for video-basedface recognition

s Object segmentation/detection.

s Motion structure. The goal of this

step is to estimate the 3D depths

of points from the imagesequence.

s 3D models for faces. Using a 3D

model to match frontal views of the

face.

s Non-rigid motion analysis.



Recentapproaches

Most video-based face recognition

system has three modules for

detection, tracking and recognition.x An access control system using Radial Basis

Function (RBS) network was proposed in

1997.

x A generic approach based on posterior

estimation using sequential Monte Carlo

methods was proposed in 2000.

x A scheme based on streaming face

recognition (SFR) was propose in August

2002.



Questions

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Face Reco.ppt

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