A Study on Face Recognition Technique Based on Eigenface

1. A STUDY ON FACE RECOGNITION
TECHNIQUE BASED ON EIGENFACE
By:
Sadique Nayeem
Pondicherry University

2. Outline
Overview
Eigenface Algorithm
Implementation
Image Database
Experimental Result
Future Enhancement
Conclusions
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3. Overview
Face recognition system consist of three component.
 Face Representation: How to model a face?
 Template-based approaches
 Feature-based approaches
 Appearance-based approaches
 Face Detection: To locate a face in image.
 Manipulation of images in “face space”
 Utilization of elliptical shape of human head
 Face Identification: Compare given image with
database.
 Performance affected by scale, pose, illumination, facial expression, and
disguise, etc.
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4. Eigenfaces Approach
 In the language of information theory…
 the main objective is to mine the relevant information in a face image,
encode it as efficiently as possible and compare one face encoding with
a database of face images encoded in the same process.
 In mathematical terms…
 Find the principal components of the face distribution, or the
eigenvectors of the covariance matrix of the set of face images, called
e ig e nface s.
 Eigenfaces are a set of features that characterize the variation between
face images. Each training face image can be represented in terms of a
linear combination of the eigenfaces, so can the new input image.
 Compare the feature weights of the new input image with those of the
known individuals
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5. Eigenface Initialization
The eigenfaces approach for face recognition involves the following
initialization operations:
 Acquire a set of training images.
 Calculate the eigenfaces from the training set, keeping only the
best M images with the highest eigenvalues. These M images
define the “face space”. As new faces are experienced, the
eigenfaces can be updated.
 Calculate the corresponding distribution in M-dimensional weight
space for each known individual (training image), by projecting their
face images onto the face space.
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6. Eigenface Recognition
Having initialized the system, the following steps are used to recognize
new face images:
 Given an image to be recognized, calculate a set of weights of the
Meigenfaces by projecting it onto each of the eigenfaces.
 Determine if the image is a face at all by checking to see if the
image is sufficiently close to the face space.
 If it is a face, classify the weight pattern as either a known person or
as unknown.
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Figure : Eigenfaces of Essex face database
-'face94'

7. Image Database
Name
of
database
Source Image
format
Image
size
Image
type
Number
of unique
individual
Total
numbe
rof
images
Variations Sample
Image
IFD IIT
Kanpur
[3]
JPG 110 X 75 Color 60 660 8 pose,
3 emotion
Essex
face
databas
e
-face94
University
of Essex,
UK [4]
JPG 90 X 100 Color 152 3040 facial
expression,
slight head
tilt.
Yale Yale
university
[5]
GIF 320 X
243
Grey 15 165 facial
expression,
w/o glasses
Face
1999
California
Institute
of
Technolo
gy [6]
JPG 300X198 Color 26 450 lighting,
expression,
background
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8. Experimental Result
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Eigenface face recognition with different sample images
Name
of
databas
e
Total
No. of
unique
person
No. of
samples
of each
image in
training
set
No. of
image in
training
set
No. of False
recognition
Accuracy rate (%)
IFD 60 1 60 31 49.18
2 120 25 59.01
3 180 16 73.77
4 240 16 73.77
5 300 12 80.32
6 360 8 86.88
7 420 3 95.08
8 480 2 96.72
9 540 1 98.36
10 600 1 98.36
11 660 1 98.36
Esse
x face
152 1 152 47 69.07
2 304 29 80.92
3 456 12 92.10
4 608 11 92.76
5 760 11 92.76
6 912 10 93.42
7 1064 10 93.42
8 1216 9 94.07
9 1368 8 94.73
10 1520 8 94.73
11 1672 6 96.05
Yale 15 1 15 8 46.66
2 30 2 86.66
3 45 3 80.00
4 60 3 80.00
5 75 2 86.66
6 90 1 93.33
7 105 2 86.66
8 120 1 93.33
9 135 1 93.33
10 150 1 93.33
11 165 1 93.33
Face
1999
26 1 26 17 34.61
2 52 15 42.30
3 78 14 46.15
4 104 9 65.38
5 130 9 65.38
6 156 8 69.23
7 182 5 80.76
8 208 5 80.76
9 234 3 88.46
10 260 2 92.30
11 286 1 96.15
Eigenface face recognition with different sample images
Name
of
databas
e
Total
No. of
unique
person
No. of
samples
of each
image in
training
set
No. of
image in
training
set
No. of False
recognition
Accuracy rate (%)

9. Experimental Result (cont..)
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Number of samples

10. Future Enhancement
 According to the experimental result, recognition with one sample
per person does not give better recognition rate in all cases.
 But, in real time application only one sample per person will be
available ( as in case of voter card, Driving license, passport or
ADHAAR Card).
 So, recognition from single sample per person is needed.
 One sample per person is easy to collect, save storage cost and
save computational cost.
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Courtesy: http://images.google.co.in/

11. Problem Statement
 This problem can be defined as follows:
“Given a stored database of faces with only one image per person,
the goal is to identify a person from the database later in time in
any different and unpredictable poses, lighting, disguise, etc
from the individual image.”
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12. Proposed Idea
 1.2 billion population of India is being enrolled for ADHAAR Card with
different biometric.
 Face image is also being collected.
 The ADHAAR Card or UID no. can be used as a platform on which
different application can be developed as under:
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ADHAAR CARD or UID NUMBER

13. Proposed Idea (contd.)
 To restrain the crime, ADHAAR Card can be the best source for
identification.
 Individual images in ADHAAR Card may work as training set.
 CCTV images from crime scene can be used as test image.
 Procedure:
 Capture the video from the CCTV camera.
 Detect the human face in the CCTV video.
 Take the CCTV image as the test image.
 Do the preprocessing on the CCTV image i.e
 Crop both the eyes, eyebrow, nose, and mouth.
 Load the ADHAAR based Face image as the training image
 Crop both the eyes, eyebrow, nose, and mouth
 Apply the Eigenface PCA for the Recognition
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14. Conclusions
 Eigenface PCA is one of the most successful technique and it gives
better result for more number of samples in training set.
 It does not produce good result for single sample per person.
 The need for real time application can be given by only single sample
per person.
 Taking ADHAAR Card as a platform, Artificial Face Recognition
system can be developed by using PCA on reconstructed image.
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15. Reference
1. “Eigenfaces for recognition”, M. Turk and A. Pentland, Jo urnalo f Co g nitive
Ne uro scie nce , vo l. 3, No . 1 , 1 9 9 1
2. “Automatic recognition and analysis of human faces and facial expressions: A
survey”, A. Samal and P. A. Iyengar, Patte rn Re co g nitio n, 25(1 ): 6 5-7 7 , 1 9 9 2
3. “The Indian Face Database”, Vidit Jain, Amitabha Mukherjee, 2002, http://vis-
www. cs. um ass. e du/~ vidit/IndianFace Database /
4. “Essex face database -face94”, University of Essex, UK,
http: //cswww. e sse x. ac. uk/m v/allface s/inde x. htm l
5. “Yale Database”, http: //cvc. yale . e du/pro je cts/yale face s/yale face s. htm l
6. “FACE 1999”, http: //www. visio n. calte ch. e du/htm l-file s/archive . htm l
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16. Thank You !
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