Face Detection Using Skin Color and Gabor Wavelet Representation

brasscoffeeAI and Robotics

Nov 17, 2013 (3 years and 9 months ago)

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Face Detection Using Skin Color and
Gabor Wavelet Representation

Information and Communication Theory Group

Faculty of Information Technology and System

Delft University of Technology

Delft
-

Netherlands

Resmana Lim, Marcel J.T. Reinders

email: resmana@it.et.tudelft.nl

Agenda of Presentation


Introduction


Extraction of Face Candidate Regions


Face Detection in Face Candidate Regions


Graph Matching by Genetic Algorithm


Experiment Results


Conclusion

Introduction


Face detection and detecting facial landmarks (such as position
of eyes, nose, mouth, etc.) play an important role in face
recognition systems



This paper focuses on the robust and accurate detection of
landmark points on the face


The approach first uses color information to detect face
candidate regions and then uses a deformable graph matching
to locate facial landmark points in these candidate regions


The method is made robust against lighting variantions and
variations between people by representing the landmark points
using Gabor filter responses


This paper introduce an alternative matching process by using a
Genetic Algorithm (GA) to optimize the matching criteria

in
finding faces candidate regions and detection of the facial
landmarks

Extraction of Face Candidate Regions


First step, to separate skin regions from non
-
skin
regions based on color information


Main objective is to reduce search space for the faces
drastically


The identification of the facial region is determined by
utilizing a priori knowledge of the skin distribution in the
HS color space


Extraction of Face Candidate Regions


Third step,
to fill the holes in the face



Finally,
small isolated regions that remain after this step
are removed


Second step,
to smoothen object silhouettes, and also
to eliminate any isolated misclassified pixels that may
appear as impulsive
-
type noise


Extraction of Face Candidate Regions

Flow Diagram of Skin Region Extraction

Extraction of Face Candidate Regions

Face Detection in Face Candidate Regions


We need identify for each face candidate region
whether it is a face or not and if so what the position of
the landmarks points are



For indentifying face candidate region is a face or not,
we apply a graph matching procedure based on
Genetic Algorithm


Matching the face candidate regions againts face
model graph


facial landmarks in the face region is found by
maximizing the simmilarity between the face model
graph and the face region image



Each facial landmark is represented by the expected
local Gabor filter responses in the image

Face Detection in Face Candidate Regions
































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Face Detection in Face Candidate Regions
















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Face Detection in Face Candidate Regions

Face model consisting of four landmark points (p1,…,p4)
represented by their Gabor filter bank responses


Graph Matching by Genetic Algorithm

Objective function








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Graph Matching by Genetic Algorithm

Solution is represented by five parameters


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scaling factor in
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direction, rotation angle


rotation angle

The threshold value of 0.7 is used to judge whether the
candidate face region constitutes a face or not.

Experiment Result

Facial Landmark localization


Conclusion


We have proposed a detection scheme for locating
facial landmarks based on color information and graph
matching using a genetic algorithm as optimization
strategy.


The performance of the proposed method was
demonstrated on various color images containing
single and multiple faces.


The results are quite promising for frontal pose faces
with moderate rotation and tilting. From the results of
the experiment, we conclude that the proposed
method has a good prospect and should be
considered in the design of face recognition systems