SIFT FLOW: Dense Correspondence across Scenes and

Transcription

FACE DETECTION
Lab on Project Based Learning
May 2011
Xin Huang, Mark Ison, Daniel Martínez
Visual Perception
Master Erasmus Mundus of Science in Vision and Robotics (VIBOT)
- X.Huang, M.Ison, D.Martínez
Contents
• Introduction
• Exploration into local invariant features
• The final method
• Results
• Conclusions
Contents
• Introduction
• Results
• Conclusions
Introduction
• Karlos Arguiñano’s TV show.
Introduction
Which is our
goal?
Introduction
• Which is our goal? -> Detect presence of a
face.
Introduction
Problem
definition
Introduction
• Problem definition:
• How can we use local invariant feature
descriptors to detect Arguinano’s face.
• Use MATLAB programming language.
Introduction
• Constraints of the project:
• Use local invariant features descriptors.
• Use MATLAB programming language.
Contents
• Introduction
• Results
• Conclusions
Exploration into local invariant features
First approaches:
1.
2.
3.
4.
5.
SURF matching.
Template database SURF matching.
Clustering
Filtering best features.
SIFT matching
First approaches:
1.
2.
3.
4.
5.
SURF matching.
Clustering
SIFT matching
1. SURF matching:
• Speeded up robust features.
• Simplification of SIFT.
• Fast.
• “Combination of novel detection,
description, and matching steps.” [ETH
Swiss Federal Institute of Technlogy]
1. SURF matching
1. SURF matching
Result with a rigid feature:
1. SURF matching
Result (no more fancy glasses):
First approaches:
1.
2.
3.
4.
5.
SURF matching.
Clustering
SIFT matching.
2. Template database SURF matching.
Test image
Classifier
(NN)
Output image
with matchings
FACE/NON FACE
templates
(SURF
descriptors)
2. Template database SURF matching
+
Result A
Result B
First approaches:
1.
2.
3.
4.
5.
SURF matching.
Clustering
SIFT matching.
3. Clustering:
Test image
Classifier
(NN)
FACE/NON FACE
Clusters of SURF
descriptors
Output image
with matchings
Clustering!
3. Clustering:
Ideal
Reality
First approaches:
1.
2.
3.
4.
5.
SURF matching.
Clustering
SIFT matching.
4. Filtering best features:
• Find the extreme ratios.
• Two separate databases.
4. Filtering best features:
Blue BAD, Orange GOOD
SURF FAILS
SURF FAILS
But...WHY?
SURF FAILS
[REF] [A Comparison of SIFT, PCA-SIFT and SURF - Luo Juan,
Oubong Gwun- International Journal of Image Processing2010]
First approaches:
1.
2.
3.
4.
5.
SURF matching.
Clustering
SIFT matching.
5. SIFT Matching:
Test image
Classifier
(NN)
FACE/NON FACE
Clusters of SIFT
descriptors
Output image
with matchings
SIFT!
5. SIFT Matching:
• Bringing in SIFT.
5. SIFT Matching:
• We should do anything else...
Contents
• Introduction
• Results
• Conclusions
The final method
1. Skin detection:
• Help SIFT as much as possible.
• Make the ROI of skin color regions.
• Methodology:
• Extract skin segments: find the mean
and covariance between CrCb.
The final method
1. Skin detection:
• Methodology:
• Obtain the Gaussian probabilities of
skin color pixels for the input image.
The final method
2. Skin detection:
• Methodology:
• Apply an adaptive threshold.
The final method
2. Skin detection:
• Methodology:
• Avoid region without holes and apply
morphological operation over the
binary image.
The final method
3. Face detection:
• Filtering best features.
• Finding matches between each skin
region and each template.
• D.Lowe
The final method
3. Face detection:
• Check if each skin region is a face.
• Threshold = Face matches / No face
matches
Contents
• Introduction
• Results
• Conclusions
Results
ans =
TestImage_num: 1
Regioin_num: 1
Keypoint_num: 1135
TotalFaceMatch_num: 99
TotalNoFaceMatch_num: 14
TemplateMatchNum: [2x37 double]
ans =
TestImage_num: 1
Regioin_num: 2
Keypoint_num: 74
TotalFaceMatch_num: 3
TotalNoFaceMatch_num: 2
TemplateMatchNum: [2x37 double]
Results
Results
Results
Contents
• Introduction
• Results
• Conclusions
Conclusions
• We have implement a face-detection method
based on feature descriptors with a lot of help.
• SIFT over SURF
• QUALITY OF THE FEATURES over QUANTITY
Conclusions
•How can be improved?
• Use shape characteristics.
• A larger template database and a more
robust classifier.
• Use morphology advantages.
• Apply other methods instead of feature
description (i.e: HAAR).
• Use previous frames information.

SIFT FLOW: Dense Correspondence across Scenes and

Transcription

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