PCA Based Geometric Modeling
1
The 11th International Conference on Computational
Science and Its Applications (ICCSA 2011), June 20-23,
2011, Santander, Spain, Accepted
PCA Based Geometric Modeling for Automatic Face Detection
Authors :
Padma Polash Paul
Presented
by
Padma Polash Paul
April 13, 2015
Outline
2

Introduction



Face Detection
Skin Color Model
Challenges in Face Detection

Background Study

Proposed Method

Experimental Results

Conclusion and Future Work
PCA Based Geometric Modeling
April 13, 2015
Face Detection vs. Skin Color Model
3
PCA Based Geometric Modeling
April 13, 2015
Challenges for Face Detection
4

Face Orientation

Multiple view Face

Background

Time in Massive Processing
PCA Based Geometric Modeling
April 13, 2015
Color Model
5

Color models for Images


RGB, HSV, YCbCr and CIE-Lab etc
RGB

triple component (RED,GREEN,BLUE)

RGB represents not only color but also luminance.

Luminance: may vary across a person's face
PCA Based Geometric Modeling
April 13, 2015
Sink Color Model
6

The common RGB representation of color images is not
suitable for characterizing skin-color.

Chromatic colors , also known as "pure" colors in the absence
of luminance

Normalized color for each pixel R, G and B can be define as
chromatic color
PCA Based Geometric Modeling
April 13, 2015
Sink Color Model (Cont’d)
7

Normalized Color
Normalized R
R
(r ) 
RG B

Normalized G
G
( g) 
RG B

Normalized (

PCA Based Geometric Modeling
b)  1  r  g
r  g  b 1
April 13, 2015
Sink Color Model (Cont’d)
8

If two points P1 [r1, g1, b1] and P2 [r2, g2, b2], are
proportional, then
r1
g1 b1


r2 g 2 b2

Then, P1 and P2 have the same color but different brightness.

Chromatic colors are well suited to segment skin regions
from non-skin regions.
PCA Based Geometric Modeling
April 13, 2015
Sink Color Model (Cont’d)
9

Skin color distribution can be represented by Gaussian Model
N (m, c)
Mean, m  E{x}
CoVar


[ x  (rb)T ]
 rr
c,   
 gr
 rg 
 gg 
From Gaussian fitted skin color model, we can find the
likelihood of skin for any pixel of an image.
Establish the threshold for Skin and non skin regions
PCA Based Geometric Modeling
April 13, 2015
Skin Region Segmentation
10

Algorithm for Skin Region Segmentation using SCM
Input image or Video (RGB)
Converting into Chromatic
Color Space
Thresholding image using
Skin Color Threshold
r = 0.38-0.52
Multiply main RGB Image by
Black and white template
g = 0.23- 0.34
Apply Region Growing
Algorithm
PCA Based Geometric Modeling
Segmented RGB skin Regions
Generate Black and White
template for skin regions
April 13, 2015
Face Detection
11
PCA Based Geometric Modeling
April 13, 2015
Face Detector
12
Proposed
Geometric
Face Detector
PCA Based Geometric Modeling
April 13, 2015
Existing Geometric Model
13

Triangle shape


Problem
 Detect non human face as face
PCA Based Geometric Modeling
April 13, 2015
Geometry of Face
14

Geometric Shape of face

Masking

More complex interior structure are estimated
PCA Based Geometric Modeling
April 13, 2015
Geometric Modeling of Face
15

Block diagram of the proposed system
Detected Skin Regions
Converting into Common
Resolution
Masking
Projecting of PCs
Calculate PCA
Reconstructing Using Smaller
Numbers of PCs
Detect Edge Using Canny Edge
Detector
Estimate Threshold Values For
Face and Non Face
PCA Based Geometric Modeling
Normalized the cumulative sum
in the rage [0 1] to get the
threshold values for face and
non face
April 13, 2015
Experimental Result
16

Database Used

California Institute of Technology (CIT)

Baoface dataset (BaoFace)

vision group of Essex University Face Database (Essex),

Georgia Tech Face Database (Georgia Tech)
PCA Based Geometric Modeling
April 13, 2015
Experimental Result
17
Face Detection Accuracy (% of Face)
Proposed Method
Dataset
Accurate Detection
Rate (%)
Threshold limits
Face Detection
Accuracy (% of Face)
Existing Best Method
Accurate Detection
Rate (%)
CIT
98.7
0.35-0.59
91.2
BaoFace
97.1
0.24-0.39
92.5
Essex
97.1
0.39-0.52
93.7
Georgia Tech
96.7
0.25-0.45
85.2
PCA Based Geometric Modeling
April 13, 2015
System Performance Result
18

For video or larger database



Processing time for face detection is important
Skin region detection is fast because of the thresholding.
Time complexity of the Face detection system is O(1).
 After

edge detection cumulative sum is compared
Threshold value is rotation invariant because we are taking the
cumulative sum of the projected geometric structure
PCA Based Geometric Modeling
April 13, 2015
Conclusion and Future Work
19




We presented a new method based on modeling of geometric
structure of the face method for automatic face detection.
Fusion of PCA based geometric modeling and SCM method
provides higher face detection accuracy and improves time
complexity.
In the future, using more complex geometric structure can be
used for better understanding of the important facial features
and threshold values.
Complex structure will also help to obtain a better and more
generalized threshold for the face.
PCA Based Geometric Modeling
April 13, 2015
20
Thanks
Any Questions?
PCA Based Geometric Modeling
April 13, 2015