The Study of Multi-Expression Classification Algorithm Based on Adaboost and Mutual Independent Feature271

facial expressions .Positive samples of human eyes and

mouth contain a variety of gestures, such as eyes open or

closed, wearing glasses, mouth open or closed, etc. Neg-

ative samples do not contain any of eyes and mouth. The

algorithm is showed as follows:

The first step:

Giving samples

112 2

,,,, ,

nn

yxy xy, i

is in-

put training sample, indicates positive sam-

ple or negative sample, L is the number of positive sam-

ples ,m is the number of negative samples. T is the num-

ber of iterations of strong classifier. t is the probabil-

ity distribution of sample weigh, and making

0,1

i

y

D

1,17,1,,,1,, 7nlDil ni .

n is the total number of samples, j is one of seven types

of expression [5]

1,

1,

i

iy

yl iy

(2)

The second step:

1) We train a weak classifier of each feature of sample

i

, whose output is multi-class.

2) In weight distribution t

D, we select the best clas-

sifier t

h from various weak classifiers, making the clas-

sification error rate minimum. The following formula ob-

taining the maximum:

'

,,

ttiti

il

rDilylhx

,l

(3)

'

,

max max,,

tt tit

il

rr Dilylhx

i

l

(4)

,

tti

hx hxl (5)

3) According to the classification performance, the

weight of classifier will be changed.

1

1ln

21

t

tt

r

ar

(6)

4) Sample weights will be updated and normalized.

1

,exp ,

,ttii

tt

Dil aylhil

Dil z

(7)

t

z is normalization constant

5) For the normalization constant t, weight distribu-

tion is made a probability density.

z

1t

D

,,exp ,

tt titi

il

zDil aylhxl

(8)

The third step:

The final strong classifier can be obtained as follows:

1

:1T

ii

t

t

iHx yc

n

The algorithm error rate fits the following inequality

on the training set at this time [4,6].

z

,

(9)

1

,

T

tt

t

xlsignahxl

(10)

3. Training and Testing of Local Features

3.1. Training

We need a large number of samples in sample training,

which is an important characteristic about Adaboost al-

gorithm. Selection of sample is very important, which

determines the effect of the classification. We divide sa-

mples into positive and negative. Eyes and mouth are

treated as mutual independent feature elements. In order

to easily obtain the weak classifiers threshold values, we

train the eyes and mouth respectively. When we get the

minimum threshold value or the maximum threshold

value of kinds of expression, chenges of eyes and mouth

will lead to huge variations. So classifiers are very diffi-

cult to get threshold values accurately and effectively.

Therefore, specific training process is divided into four

steps [7]. The first step, we use the positive samples to

train, obtaining the threshold values quickly. The second

step, in order to adjustment the threshold values appro-

priately, we use whole face images to train. As the false

detection always occurs in eye or mouth, we use eye im-

ages and mouth images to further adjust the threshold

parameters in third step. The fourth step is to use the new

negative sample proposed in the paper to reduce the false

detection rate.

3.2. Detection

The specific detection process is as follows: when there

is a image to detecte, we use different rectangular boxes,

whose sizes are generally from small to large, to scan the

whole image. The size of the smallest rectangle box is

normalized, For example 24 × 24 pixel. Each rectangular

box move a pixel from right to left until it reaches edge

of the image. When rectangular box scans the whole im-

age at this level, rectangular box enlarge a certain pixel

to next scanning. Therefore the largest rectangle box is

several times larger than the smallest rectangular box.

During the scan, each rectangular area can be carried out

classification decision. The purpose of scan is to find a

specific facial feature region. Therefore, if detection area

can be adopted by the classification, it proves to find a

person’s eyes or mouth, else detection will be stopp ed. If

any of rectangular area can not be adopted by the classi-

fier, the region does not exist any eyes or mouth. Of

course, when dimensions of the rectangular area enlarge

in proportion, threshold parameters of weak classifier

also amplified by the same proportion.

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