Face Reco.ppt
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aceRecognition
Presented By:-
Yogen Sharma
1509262
EC-6
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Contents
s Face Segmentation/Detection
s Facial Feature extraction
s
Face Recognitions Video-based Face Recognition
s Comparison
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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
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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).
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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”.
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Example of a systemusing EBL
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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?
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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.
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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.
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Test results of using NN
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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.
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Comparison withEBL
s Test results reported in 1997.
s Using two test sets (155 faces).
SVM achieved better detection rateand fewer false alarms.
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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!
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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.
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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.
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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.
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Questions