5. Face Recognition Using Eigen Features of Multi-Scale ...shodhganga.inflibnet.ac.in/bitstream/10603/3478/15/15_chapter 5.pdf · 5. Face Recognition Using Eigen Features of Multi-Scale

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5. Face Recognition Using Eigen Features of Multi-Scale Face Components and Artificial Neural Networks

Automated face recognition aims at exploiting face images to

identify human subjects. A number of approaches such as appearance

/holistic based, feature / component based and hybrid face recognition

approaches have been proposed in literature for automated face

recognition and these three approaches are discussed in detail in the

second chapter. The following section deals with an overview of hybrid

face recognition techniques existing in literature and the proposed

method.

5.1) Hybrid Approach for Face Recognition

In hybrid face recognition approach, either both holistic and

component information are used for recognition or it is defined as

combination of different face recognition methods. In paper [7] Mehrtash

et.al proposed a hybrid recognition system which tries to find the human

recognition behavior by efficiently combining the facial components in

the recognition task. In this method the decision system uses the

weighted majority strategy to fuse the results of whole image and facial

component classifier. Simulation studies justify the superior performance

of the proposed method as compared to that of Eigenface method.

In paper [6] Mazloom et.al presented a hybrid approach for face

recognition by handling three issues put together. For pre-processing

and feature extraction stages, a combination of wavelet transform and

PCA are applied, and for classification, the Neural Network (MLP) is

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explored for robust decision in the presence of wide facial variations. The

experiments that have been conducted on the ORL database indicated

that the combination of Wavelet, PCA and MLP exhibited an excellent

performance. This is on account of the fact that it has the lowest overall

training time, the lowest redundant data, and the highest recognition

rates, compared to similar methods introduced so far. The proposed

method, in comparison with the present hybrid methods, enjoys a low

computation load in both training and recognizing stages.

In paper [8], Suvendu Mandal et.al has proposed a hybrid face

recognition system. The proposed method combines the structural

features with holistic features. In that experiment, the facial image is

partitioned into a number of petals considering nose tip as the origin.

From each petal, intensity profile is computed by radial projection and

then they are more compactly represented by applying Discrete Cosine

Transform (DCT) on them. To classify the images, correlation between the

vectors is used as the „similarity‟ measure. The proposed method is

simple to implement and is efficient. The performance of the proposed

method is encouraging and gives scope for further study.

A couple of hybrid approaches which use both holistic and local

features are also proposed in literature: Local Feature Analysis (LFA) is

an interesting biologically motivated method [106]. It demonstrates that

using the same number of kernels, the perceptual reconstruction quality

of LFA based on the optimal set of grids is better than that of PCA. But

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no results on recognition performance based on LFA are reported. An

interesting shape model called Active Shape Model (ASM) [107]. In this

method both shape and grey level information are modeled and used. It

uses statistical models of the shapes of various face components which

iteratively deform to fit to an example shape in a new image.

Performance in terms of percentage of recognition for different

hybrid face recognition methods exists in literature are given by table

number 5.1, using eigen features of multi-scale face components and

artificial neural networks has shown better results compared to the

existing similar methods.

Table 5.1 – Performance Comparison of different hybrid face recognition techniques on ORL database.

S.No Method Percentage of

Recognition

1 Scale-robust feature extraction for face

recognition. (high resolution100X100) (low resolution12X14). [11]

90% 80%

2 Weighted modular principal component

analysis. [7] 87%

3 Hybrid method based on structural and

holistic. [21] 90.30%

4 Hybrid model using facial components.

[22] 88.57%

5 PCA, LDA and Neural Network for face

identification. [23] 95.80%

In the present work, a hybrid method for face recognition is

proposed in which facial components such as eyes, nose and mouth are

extracted from pre-processed face image using cropping technique. These

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features and the whole face are down sampled with different resolutions

and a feature vector is formed, which is projected on eigen feature space

or fisher feature space to get weight vector and this vector is used as

input to the Artificial Neural Network for face classification.

5.2) Proposed Algorithm for Face Recognition

In this study, hybrid face recognition technique is proposed by

combining both componential cues (such as eyes, mouth, and nose) and

holistic information to improve the face recognition rate at different

resolutions.

In the proposed method, initially face images are pre-processed

(average filtering, histogram equalization and image resizing using Bi-cubic

Interpolation) then face features (eyes, nose and mouth) are extracted

manually by cropping techniques, then image patches of eyes, nose, mouth

and the whole face are down sampled using different sampling rates based

on component significance in recognition process (Ex eye 1:1, nose 1:2,

mouth 1:4 and whole face 1:8). These 2D face image patches of different

components are combined and represented by a matrix 𝐴 = [𝑎1 , 𝑎2 , … … 𝑎𝑖 , ]

where 𝑎1 represents a one-dimensional image column vector obtained by

scanning the two-dimensional image patches in lexicographic order and

writing them into a column vector (it is called feature vector).

Subspace analysis PCA or LDA are employed to the matrix A. PCA is

used for further dimensionality reduction and good representation, and

LDA is used to get better discrimination information.

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The weight vector obtained by projecting training face on PCA or LDA

feature space is used as input to the Artificial Neural Network which is

trained by Back-propagation (BP) or Radial basis function (RBF) network

for recognizing faces. The flow chart and implementation steps for the

proposed face recognition technique are given as follows.

5.3) Flow chart for Proposed face recognition method using Eigen features of multi-scale face components and ANN (Figure 5.1)

5.4) Implementation of Algorithm for the Proposed Face Recognition Technique

The algorithm is described in the following steps -

ANN Training for face

classification

Face Data Base

Average filtering, histogram equalization and image

resizing

Feature Extraction by cropping face image

Multi-scaling of facial features

with different resolutions

Forming face feature vector for

each face in data base

Projecting feature vectors on

PCA/LDA space to find weight vector

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5.4.1) Training Phase

1. Given face image is preprocessed and normalized .

2. Face image is re-sized by using Bi-cubic interpolation method.

3. The face ellipse is extracted by applying a standard mask.

4. Eyes, nose and mouth patches are cropped using image cropping

techniques and the locations of the eyes, nose and mouth patches are

kept same for all images in training and test database.

5. Dimensions of face components are reduced by down sampling face

comoponents with different resolution ratios based on component

significance in recognition process.

6. The remaining portion of the face is extracted by removing the face

component patches from the whole face and remaining portion of face or

the whole face are down sampled with higher sampling ratios as this

component is not much significant compared to the other components

such as eyes, nose and mouth.

7. Feature vector is obtained by scanning image patches obtained in (4),

(5) and (6) in lexicographic order and writing them into a single column

vector of dimension N x 1.

8. Feature weight vector is found by projecting each face feature vector

on PCA or LDA feature space.

9. Weight vector found in step 8 is used as input to artificial neural

network classifier and it is trained to classify the faces using back

propagation or RBF network algorithm.

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5.4.2) Testing Phase

In testing phase, face image is selected from test data base and

steps 1 to 8 are carried out as it is done in training phase. But in the

last step, training of Artificial Neural Network is not required, in which

weight vector is directly fed to the already trained Artificial Neural

Network to identify the test face with one of the training face images.

5.5) Results and Discussion 5.5.1) Simulation Result

In the proposed face recognition algorithms, experiments are

conducted on both ORL and FERET databases. In ORL database 400

face images of 40 subjects are considered, performance of face

recognition techniques is measured in terms of percentage of recognition

and it is calculated by averaging results for 10 independent runs of the

program, where different combination of training and testing face images

are used. In FERET database only frontal face images are considered and

results are calculated only for one run of program. For evaluating

performance of partial face recognition, percentage recognition is

calculated with respect to resolution of face image, down sampling

resolution ratios of face components, variation in training set, number of

hidden layer nodes, and number of principal components.

Specifications of ANN architecture and training algorithms are selected as follows:

Number of Layers : 3 (input layer, hidden layer, output layer)

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Number of input nodes : It is equal to number of elements in

feature vector

Number of output nodes : The number of face images to be

trained

Number of hidden : In RBFN, it is selected not exceeding to

layer nodes the maximum number of inputs.

In BPNN, it is limited to the maximum

number equal to the average of output

node and input nodes

Learning algorithms (BPNN) : Batch Gradient descent with

used in training process momentum (using „traingdm‟ in matlab)

Learning algorithms (RBFN) : Gradient descent

used in training process (using „newrb‟ in matlab)

Learning rate range : 0.01 to 1

Mean Square Error range : 0.01 to 0.001

Transfer functions : Logsig in BPNN

between input and Radial Basis Function in RBFN

hidden layer

Transfer functions : Logsig in BPNN

between hidden layer Logsig in RBFN

and output layer

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Table 5.2 – Performance of face recognition w.r.t number of training faces for four different methods

a) Specifications of face recognition system for ORL database

Data Base ORL

Image Size 56X46

Multi-scaling Eye-1:1, Nose-1:2,

Mouth-1:4, Whole Face-1:8

ANN Structure 40: 50:40 (for

LDA39:50:40)

Number of Training

Faces for each class

Variable

Number of Testing Faces

Variable

Number of Principle Components

40 (for LDA 39)

b) Performance evaluation on ORL database

Number of Percentage of recognition

Train faces

Testing faces PCA

+BPNN PCA +RBFN

LDA +BPNN

LDA +RBFN

2 3 4

5 6 7

8

8 7 6

5 4 3

2

84.50 86.10 93.20

96.35 96.95 97.10

98.00

84.70 87.35 94.85

96.55 97.00 97.35

98.10

83.75 85.65 93.00

96.85 97.10 97.55

98.20

84.00 87.00 93.10

97.10 97.50 97.95

98.40

134

Figure 5.2a – Performance of face recognition w.r.t number of training faces for four different methods (ORL

database) c) Specifications of face recognition system for FERET database

d) Performance evaluation on FERET database

83

85

87

89

91

93

95

97

99

2 3 4 5 6 7 8

Pe

rce

nta

ge o

f R

eco

gnit

ion

Number of Training Faces

PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

Data Base FERET

Image Size 56X46

Multi-scaling Eye-1:1, Nose-1:2, Mouth-1:4, Whole Face-1:8

ANN Structure 40: 50:40 (for LDA39:50:40)

Number of Training Faces for each class

Variable


Variable

Number of Principle

Components

40 (for LDA 39)

Number of faces Percentage of recognition

Train faces

Testing faces PCA

+BPNN

PCA

+RBFN

LDA

+BPNN

LDA

+RBFN

2

3 4

3

2 1

95.50

97.15 97.65

96.00

97.80 98

95.15

98.00 98.25

94.30

98.20 98.50

135

Figure 5.2b – Performance of face recognition w.r.t number of training faces for four different methods (FERET

database)

From the results cited in table number 5.2, the following inferences can

be made -

Face recognition percentage increases with number of training faces

in all four face recognition methods, because the Artificial neural

network adaptability capacity to recognize faces increases with

increasing number of training faces.

When number of training faces in each class is small compared to

testing faces, PCA face recognition method outperforms LDA. When

number of training faces is larger in each class, LDA outperforms

PCA.

LDA+RBFN method of face recognition outperforms the other methods

(98.5% for FERET 98.4% for ORL) as face discrimination ability of

92

93

94

95

96

97

98

99

2 3 4

Per

cen

tage

of

Rec

ogn

itio

n


PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

136

LDA is more compared to PCA whereas RBF network classification

efficiency is better compared to BPNN.

When numbers of training and testing faces are equal to 5 (on ORL

database), the face recognition percentage is found to be better

compared to the other similar face recognition methods in literature

(Table 5.1).

Table 5.3 – Performance of face recognition w.r.t Resolution of face components for four different methods


b) Performance comparison of PCA+BPNN, PCA+RBFN, LDA+BPNN and LDA+RBFN methods

Data Base ORL

Image Size 56X46

Multi-scaling Variable


LDA39:50:40)


5

Number of Testing Faces 5


40 (for LDA 39)

Row No.

Eye (L)

Eye (R)

Nose Mouth Whole face

Percentage of recognition

PCA +BPNN

PCA +RBFN

LDA +BPNN

LDA +RBFN

1 2

3 4 5

6

1:1 1:1

1:2 1:2 1:2

1:1

1:1 1:1

1:1 1:2 1:2

1:1

1:2 1:4

1:4 1:8 1:2

1:2

1:4 1:8

1:8 1:8 1:4

1:8

1:8 1:16

1:8 1:16 1:8

1:16

96.35 93.75

88.60 84.45 85.00

89.95

96.55 94.00

87.00 85.10 85.20

91.00

96.85 94.10

88.10 85.70 88.80

93.60

97.10 94.25

88.40 87.15 87.50

94.10

137

Figure 5.3a – Performance of face recognition w.r.t Resolution of face components for four different methods (ORL

database)

c) Specifications of face recognition system for FERET database

d) Performance comparison of PCA+BPNN, PCA+RBFN, LDA+BPNN

and LDA+RBFN methods Row No

Eye(L) Eye(R) Nose Mouth Whole face


PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

1 2 3 4 5 6

1:1 1:1 1:2 1:2 1:2 1:1

1:1 1:1 1:1 1:2 1:2 1:1

1:2 1:4 1:4 1:8 1:2 1:2

1:4 1:8 1:8 1:8 1:4 1:8

1:8 1:16 1:8 1:16 1:8 1:16

97.15 94.10 90 85.10 85.90 90.25

97.80 94.70 88.10 86.25 86.30 92.35

98 95.25 89 86.30 89.25 94.20

98.20 95.40 89.15 87.70 88.10 94.40

75

80

85

90

95

100

Set1 Set2 Set3 Set4 Set5 Set6

Per

cen

tage

of

Rec

ogn

itio

n

Resolution of face components

PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

Data Base FERET

Image Size 72X48

Multi-scaling Variable


LDA39:50:40)

Number of Training

Faces for each class

3



40 (for LDA 39)

138

Figure 5.3b – Performance of face recognition w.r.t Resolution of face components for four different methods (FERET

database)


be made -

Percentage of face recognition decreases with decrease in resolution

for eyes, nose, mouth and the whole face for all the four methods.

LDA+RBF method of face recognition outperforms other methods with

percentage of face recognition (97.10% for ORL, 98.20% for FERET)

when resolution of eye is 1:1, nose is 1:2, mouth is 1:4 and the whole

face is 1:8

It can be seen from the 1st and 5th row of results that the change in

resolution of eye results in drastic change in the percentage of

recognition of face.

75

80

85

90

95

100


Per

cen

tage

of

Rec

ogn

itio

n

Resolution of face components

PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

139

It can be observed from 1st and 6th row of results that the change in

resolution of mouth and whole face changes face recognition rate,

only slightly.

Table 5.4 – Performance of face recognition w.r.t Resolution of face

image a) Specifications of face recognition system for ORL database

b) Performance evaluation on ORL database:

Data Base ORL

Image Size Variable



ANN Structure 40: 50:40 (for LDA39:50:40)


5


5


40 (for LDA 39)

Resolution of Face


PCA +BPNN

PCA +RBFN

LDA +BPNN

LDA +RBFN

28X23 42X35 56X46

84X69 112X92

85.10 90.05 96.35

96.60 97.00

87.30 91.25 96.55

97.00 97.15

88.00 91.40 96.85

97.50 98.10

88.30 92.00 97.10

98.00 98.25

140

Figure 5.4a – Performance of face recognition w.r.t Resolution of face image (ORL database)

c) Specifications of face recognition system for FERET database

d) Performance evaluation on FERET database:

75

80

85

90

95

100

28X23 42X35 56X46 84X69 112X92

Per

cen

tage

of

Rec

ogn

itio

n

Resolution of Face

PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

Data Base FERET

Image Size Variable

Multi-scaling Eye-1:1, Nose-1:2, Mouth-

1:4, Whole Face-1:8

ANN Structure 40: 50:40 (for LDA

39:50:40)


for each class

3



40 (for LDA 39)

Resolution of Face


PCA+

BPNN

PCA+

RBFN

LDA+

BPNN

LDA+

RBFN

24X16 48X32

72X43 144X96

192X128

85.70 90.50

97.15 97.20

97.25

88 92

97.80 97.95

98

88.60 92.20

98 98.05

98.10

89 92.50

98.20 98.30

98.40

141

Figure 5.4b – Performance of face recognition w.r.t Resolution of face image (FERET database)


be made -

Percentage of face recognition increases with increase in resolution of

face image for all the four methods.

LDA+RBF method outperforms other methods with percentage of face

recognition 98.25% when face image resolution is 112X92 for ORL

database, 98.40% for FERET database when face image resolution is

192X128.

The face recognition rate increases with the face image resolution and

levels off when arriving at one certain resolution (56X40 for ORL and

72X43 for FERET) it is concurring with conjecture that the face

discrimination information increases with the face image resolution

and remains stable when reaching one specific resolution.

75

80

85

90

95

100

24X16 48X32 72X43 144X96 192X128

Per

cen

tage

of

Rec

ogn

itio

n

Resolution of Face

PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

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Table 5.5 – Performance of face recognition w.r.t Variation in training set of face images for ORL database


b) Performance evaluation on ORL database:

Average recognition rate= 97.25 97.46 97.55 97.67

Refer section 3.5 for different face images in ORL and FERET database.

Data Base ORL

Image Size 56X46

Multi-scaling Eye-1:1, Nose-1:2, Mouth-1:4, Whole Face-1:8

ANN Structure 40: 50:40


5: by changing different faces in set

Number of Testing Faces 5: by changing

different faces in set

Number of Principle

Components

40

Set Training Set

Testing Set Percentage of recognition

PCA

+BPNN

PCA

+RBFN

LDA

+BPNN

LDA

+RBFN

1

2 3 4

5 6

1,2,3,4,5

2,3,4,5,6 3,4,5,6,7 4,5,6,7,8

5,6,7,8,9 6,7,8,9,10

6,7,8,9,10

1,7,8,9,10 1,2,8,9,10 1,2,3,9,10

1,2,3,4,10 1,2,3,4,5

96.10

97.00 97.25 98.50

98.30 96.35

96.20

97.25 97.40 98.85

98.55 96.55

96.25

97.95 98.30 98.00

98.00 96.85

96.85

98.00 98.40 98.20

97.50 97.10

143

Figure 5.5 – Performance of face recognition w.r.t Variation in training set of face images for ORL database


be made -

The face recognition rate changes based on training set and testing set

used in recognition process.

The maximum face recognition rate (98.85%) is achieved when training

set and testing set are balanced with respect to facial expression and

spectacles that is 4,5,6,7,8 images are used for training and 1,2,3,9,10

images used for testing, for the above mentioned sets PCA methods

outperforms the LDA methods as discrimination information is found to

be less in training set.

When discrimination information is high in set number 1 and 6, the

percentage of face recognition is found to be the maximum and LDA+RBF

method outperforms other methods.

94.5

95

95.5

96

96.5

97

97.5

98

98.5

99

99.5


Per

cen

tage

of

Rec

ogn

itio

n

variation in training set

PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

144

Table 5.6 – Performance of face recognition w.r.t ANN structure for ORL database

a) Specifications of face recognition system ORL database


Data Base ORL

Image Size 56X46

Multi-scaling Eye-1:1, Nose-1:2, Mouth-1:4, Whole

Face-1:8

ANN Structure Variable


5

Number of Testing

Faces

5

Number of Principle

Components

40

Number of Hidden layer

nodes

ANN Structure


PCA +BPNN

PCA +RBFN

LDA +BPNN

LDA +RBFN

10 20

30 40 50

70

40:10:40 40:20:40

40:30:40 40:40:40 40:50:40

40:70:40

90.65 91.05

95.25 95.30 96.10

94.50

91.15 93.10

95.75 95.80 96.25

95.25

91.45 93.25

95.95 95.95 96.60

95.50

91.65 93.55

96.85 96.85 97.10

96.00

145

Figure 5.6 – Performance of face recognition w.r.t ANN structure for

ORL database

*For LDA instead of 40 inputs 39 inputs are used in ANN structure


be made -

The face recognition rate increases with the number of hidden layer

nodes, because dimension of weight matrix increases with hidden layer

nodes, but after certain dimension the percentage of recognition

decreases and maximum recognition rate is achieved for 40:50:40 ANN

structure.

LDA+RBF outperforms other methods as the discrimination information

is high in training set (1, 2, 3, 4 & 5).

90

91

92

93

94

95

96

97

98

10 20 30 40 50 70

Per

cen

tage

of

Rec

ogn

itio

n

Number of hidden layer nodes

PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

146

Table 5.7 – Performance of face recognition w.r.t Number of principle components for ORL database



Data Base ORL

Image Size 56X46



ANN Structure Variable


5

Number of Testing

Faces

5

Number of Principle

Components

Variable

Number of principal components

ANN Structure


PCA

+BPNN

PCA

+RBFN

LDA

+BPNN

LDA

+RBFN

15

25 30

35 40 50

100

15:40:40

25:40:40 30:35:40

35:50:40 40:50:40 50: 40:40

100:60:40

60.00

94.60 95.25

95.50 95.50 96.35

92.00

62.05

94.60 93.50

95.00 95.90 96.55

93.00

63.25

94.15 94.20

96.20 96.10 96.85

94.00

67.85

94.90 94.90

96.95 96.50 97.10

94.25

147

Figure 5.7 – Performance of face recognition w.r.t Number of principle components for ORL database

*For LDA 39:50:40 ANN structure is used.


be made -

The face recognition rate increases with principle components up to

30, which are 15% of 200 faces and levels off thereafter. But once

principle components cross 50, the percentage of recognition

decreases due to the complexity of ANN structure.

LDA+RBF outperform other methods and achieve maximum

recognition rate of 97.10% for 40 principle components, when

structure of ANN is 40:50:40.

60

65

70

75

80

85

90

95

100

15 25 30 35 40 50 100

Per

cen

tage

of

Rec

ogn

itio

n


PCA+BPNN

PCA+RBFN

LDA+BPNN

LDA+RBFN

148

5.6) Conclusions

In the proposed work, face recognition method using feature weight

vector and Artificial Neural Network (BPNN or RBFN) classifier, resulted in

four face recognition methods: 1) PCA+BP 2) PCA+RBF 3) LDA+BP 4)

LDA+RBF and performance is evaluated for the above mentioned four

techniques on FERET & ORL databases in terms of percentage recognition

with respect to resolution of face components, resolution of face, number of

training faces, variation in training set, Artificial Neural Network structure

and number of principal components. Based on the results and analysis,

the following conclusions are made -

LDA+RBF method of face recognition outperforms the other methods

(with 98.40% recognition rate), as face discriminating ability of LDA is

higher compared to PCA, and RBF network classification accuracy is

more compared to back propagation neural network.

The face recognition percentage is at its highest with 97.10%

recognition rate for LDA+RBFN method, for low resolution face image

(56X46) with multi-scaling of eye 1:1, nose 1:2, mouth 1:4 and whole

face 1:8, when number of training and testing faces are equal to 5 on

ORL database. This percentage of recognition is the highest compared

to the other similar hybrid methods of face recognition in literature.

Percentage of face recognition increases with the number of training

faces in all four face recognition methods (PCA+BPNN, PCA+RBFN,

LDA+BPNN, LDA+ RBFN).

149

Face recognition rate increases in all four methods of face recognition

with resolution of face image and levels off, when arriving at one

certain resolution (56X40). It is concurring with conjecture that the

face discriminate information increases with the face image resolution

and remains stable when reaching one specific resolution.

Change in resolution of eyes changes the face recognition rate

drastically, whereas change in resolution of nose, mouth and whole

face changes the recognition rate slightly.

In training set of face images, if discriminant information is high, LDA

method outperforms PCA, and when discriminant information is low,

it is vice-versa.

The face recognition rate increases with number of hidden layer

nodes, because dimension of weight matrix increases with hidden

layer nodes, however, after reaching certain dimension, the

percentage of recognition decreases and maximum recognition rate is

achieved for 40:50:40 ANN structure.

The face recognition rate increases with principle components upto 30,

which are 15% of 200 faces and levels off. Once a principle component

crosses 50, the percentage of recognition decreases due to the

structure of ANN.

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5. Face Recognition Using Eigen Features of Multi-Scale ...shodhganga.inflibnet.ac.in/bitstream/10603/3478/15/15_chapter 5.pdf · 5. Face Recognition Using Eigen Features of Multi-Scale