Download - SIFT FLOW: Dense Correspondence across Scenes and Its ... · SIFT matching Master Erasmus Mundus of Science in Vision and Robotics (VIBOT) - X.Huang, M.Ison, D.Martínez. Exploration

FACE DETECTIONLab 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


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. SURF matching.

2. Template database SURF matching.

3. Clustering

4. Filtering best features.

5. 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 matchingResult with a rigid feature:



1. SURF matchingResult (no more fancy glasses):






3. Clustering


5. SIFT matching.




Test imageClassifier

(NN)Output image

with matchings

FACE/NON FACE templates

(SURF descriptors)


2. Template database SURF matching


+


2. Template database SURF matching Result A



2. Template database SURF matching Result B






3. Clustering


5. SIFT matching.


3. Clustering:




(NN)Output image

with matchings

FACE/NON FACE Clusters of SURF

descriptors

Clustering!

3. Clustering:

Ideal Reality






3. Clustering


5. 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 Processing-2010]




3. Clustering


5. SIFT matching.


5. SIFT Matching:




(NN)Output image

with matchings

FACE/NON FACE Clusters of SIFT

descriptors SIFT!

5. SIFT Matching:

• Bringing in SIFT.



5. SIFT Matching:

• We should do anything else...



Contents

• Introduction



• Results

• Conclusions


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.


The final method

• D.Lowe

3. Face detection:

• Check if each skin region is a face.

• Threshold = Face matches / No facematches


The final method

Contents

• Introduction



• Results

• Conclusions



Resultsans =

TestImage_num: 1Regioin_num: 1Keypoint_num: 1135

TotalFaceMatch_num: 99TotalNoFaceMatch_num: 14

TemplateMatchNum: [2x37 double]

ans =

TestImage_num: 1Regioin_num: 2Keypoint_num: 74

TotalFaceMatch_num: 3TotalNoFaceMatch_num: 2

TemplateMatchNum: [2x37 double]


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.


Download - SIFT FLOW: Dense Correspondence across Scenes and Its ... · SIFT matching Master Erasmus Mundus of Science in Vision and Robotics (VIBOT) - X.Huang, M.Ison, D.Martínez. Exploration

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