# EE368 Digital Image Processing Face Detection Project

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```							EE368 Digital Image Processing
Face Detection Project

By
Gaurav Srivastava
Siddharth Joshi
Problem Definition

   To detect faces in a class group
photograph.

   To differentiate female faces.
Challenges
   Varying lighting conditions.
   Various objects with pseudo-skin color.
   Occluded faces.
   Different scale size of faces.
   Faces in non-frontal position.
Approach

Input
Morphological
Image       Skin Color                                        Eigenspace
Operations
Segmentation      (Hole Filling, Erosion)           Projection

Detecting                                   Density Estimation
Deciding
Male/Female                                         And
Face/Non-face
Faces                                      Peak Detection
Output
Image

Block Diagram of Implementation
Skin Color Segmentation
   YCbCr Space
   Better Skin Color
localization than
HSV space.
   Invariant under
various lighting
conditions.
Result of Skin Color Segmentation
Morphological Operations
   Hole Filling.
   1st Level Erosion, Diamond structuring
element.
   2nd & 3rd Level Column Erosion.
   Selection of blocks, by size criterion.
Binary Image After Hole Filling
Different Levels of Erosion
Eigenspace Decomposition
   Training set of 53
facial images for KL
Transform.
   First 20 eigenvectors
used as Principal
Components.
Gaussian F-space Density Estimation
   Estimation of the likelihood function for the
image data – i.e. P(x|).

P( x | )  PF x | PF x | 
ˆ                     ˆ
   ˆ
P( x | ) can be used to compute a local
measure of the target saliency.
(i, j ) ML  arg max{S (i, j; )}

S (i, j; )  P( x | )
Detected Face   Probability Density
RMS Detection Criterion
   Difference in
reconstruction errors
for Face/Non-face
using eigenspace
projections.
Gender Determination
   Projection calculations using multiple
faces of a female.
   Calculation of RMSE of projections of a
facial candidate with stored projections.
k
MSE x   ( y x  yik ) ( y x  yik )
T

i 1

MSE~  min( MSE x )  ~  Fi
x                  x
Original Image
Detected Faces: Male/Female
Conclusions
   Combination of deterministic algorithms
like PCA, F-space density estimation
and heuristics.
   Difficult to generalize the algorithm.
   Algorithm performs well on most frontal
faces.
   Difficulty in detecting occluded faces.
Face Detection   Gender Recognition
1        2 (19)             2 (1)
2        1 (20)             3 (0)
3        3 (18)             3 (0)
4        3 (18)             3 (0)
5        8 (13)             3 (0)
6        5 (17)             3 (0)
7        6 (16)             3 (0)
8        7 (14)             1 (2)
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