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

doi:10.4236/jsip.2011.24038

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Journal of Signal and Information Processing, 2011, 2, 270-273

doi:10.4236/jsip.2011.24038 Published Online November 2011 (http://www.SciRP.org/journal/jsip)

The Study of Multi-Expression Classification

Algorithm Based on Adaboost and Mutual

Independent Feature

Liying Lang, Zuntao Hu

Department of Electrical and Information, Hebei University of Engineering, Handan, China.

Email: [email protected]

Received June 20th, 2011; revised August 13th, 2011; accepted August 22nd, 2011.

ABSTRACT

In the paper conventional Adaboost algorithm is improved and local features of face such as eyes and mouth are sepa-

rated as mutual independent elements for facial feature extraction and classification. The multi-expression classifica-

tion algorithm which is based on Adaboost and mutual independent feature is proposed. In order to effectively and

quickly train thresho ld values of weak classifiers of fea tures, Sample of training is carried out simple improvement. We

obtain a good classification results through experiments.

Keywords: Adaboost Multi-Expression Classification Algorithm, Local Feature, Feature Extraction, Sample Training

1. Introduction

Due to increasingly wide application, Facial multi-ex-

pression classification which is widely concerned by gov-

ernments and research institutes is becoming a challeng-

ing research topic in field of pattern recognition. Cur-

rently there are many main methods used for facial ex-

pression classification, such as Euclidean distance, Sup-

port vector machine (SVM), Neural network(NN), Hid-

den Markov(HMM), Adaboost and Linear discriminant

analysis (LDA), etc. Adaboost algorithm which owns

higher speed and higher detection rate has been success-

fully applied in the field of face detection. Most of facial

expression changes exist in eyes and mouth, so features of

eyes and mouth are treated as mutual independent ele-

ments [1]. The method can greatly reduce redundancy and

improve the speed of training threshold values. However,

if we want to use the algorithm to facial multi-ex pression

classification, there is a main problem. When error recog-

nition rate of weak classifiers of Adaboost is higher,

overall recognition rate of the algorithm will reduce to

zero exponentially, key problem is to train a weak classi-

fier threshold value accurately and fast. So we improve

training samples, and negative samples is proposed in this

paper [2]. Threshold value of weak classifier is the most

crucial part. Positive samp les contain on ly images of eyes

or mouth, while negative samples are removed eyes and

mouth. So the multi-expression classification algorithm

which is based on Adaboost and mutual independent fea-

ture is proposed. Experimental results prove that false

recognition rate is almost close to zero.

2. Adaboost Multi-Expression Classification

Algorithm

Basic idea of Adaboost algorithm is to use a large num-

ber of weak classifiers to add up together to form a very

strong classification through a certain method. In the

algorithm, each training sample is assigned a weight, and

it demonstrates a probability of some weak classifiers

which can be selected into training set. If a sample is

accurately classified by the current weak classifier, its

weight will be reduced. On the contrary, if a sample is

not properly classified, the weight is to be raised accord-

ingly. In this way, Adaboost algorithm can focus on more

difficult samples [2,3].

A weak classifier





x consists of the following

three parts: rectangle feature value



x, a classifica-

tion threshold value



and a direction sign

p (

0,1). They are in line with the following relation ship [4]:







,, 1,2,3,4,5,6,7

jj jj

jpf xpj

Hx else











 (1)

We obtain the minimum threshold value



min



and

the maximum threshold value



max



of eyes and mo-

uth, making

 

min max



ifi







.





xis one of seven

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

,,,, ,

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

y



1,17,1,,,1,, 7nlDil ni .

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

of expression [5]



yl iy





 (2)

The second step:

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

, 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

rDilylhx



(3)









max max,,

tt tit

rr Dilylhx





(4)







tti

hx hxl (5)

3) According to the classification performance, the

weight of classifier will be changed.

1ln













(6)

4) Sample weights will be updated and normalized.

 











,exp ,

,ttii

Dil aylhil

Dil z





 (7)

z is normalization constant

5) For the normalization constant t, weight distribu-

tion is made a probability density.

D













,,exp ,

tt titi

zDil aylhxl

 (8)

The third step:

The final strong classifier can be obtained as follows:





:1T

iHx yc

n





The algorithm error rate fits the following inequality

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

(9)

 

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.

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

272

4. Experimental Results and Analysis

4.1. Facial Expression Image Database

We selected Japanese women expression database of the

Kyushu University JAFFE and Our self-built facial ex-

pression database in experiments. The JAFFE database is

made up 213 individual images of ten persons, and each

person shows anger, disgust, fear, happiness, sadness,

surprise and neutral. There are 2 - 4 images for every

face expression, and images are all 256 × 256 grayscale

images. Our self-built database composes of seven indi-

viduals, each person also shows seven basic facial ex-

pressions, and pictures are also 128 × 128 grayscale ima-

ges [3,8]. As images obtain from two different databases,

there are very huge difference. To be able to quickly and

accurately classify the facial expression, images need

appropriate pretreatment. In this paper, we use geometric

normalization and the intensity normalized to improve

the image quality. We get the mouth images, the eye im-

ages and the whole face images. Figure 1 shows the im-

ages selected from the JAFFE database and self-built

database. Figure 2 shows the pretreatment image and the

negative sample.

4.2 Experimental Results

We selected 120 facial expression images of six persons

from JAFFE database and selected 42 images of three

persons from self-built database in experiments. Table 1

shows different experimental results between JAFFE da-

tabase and self-built datab ase. Figure 3 shows the recog-

nition rates of the seven different facial expressions(1 is

on behalf of happiness, 2 is neutral, 3 is sadness, 4 is sur-

prise, 5 is disgust, 6 is angry, 7 is fear). It is can be seen

from the Table 1 that recognition rates in JAFFE are

much more higher than the self-built database. The rea-

son is that the facial expression in self-built database is

not exaggerated, resulting in a huge difference between

the experimental results.

4.3. Experimental Analysis

In experiments, the number of iteration is set 60. With

the number of iterations changing, the error recognition

rate will be very different. We compared two different

expressions in a group, the experimental results show

that recognition rate of happiness and surprise are more

higher than disgust, sadness and neutral. The reasons are

that facial expression changes of happiness or surprise is

much more obvious, feature extraction is more easier,

and classification get a small error. What’s more, it can

be seen from Figure 4 that the error recognition rate of

the first 30 iterations reduces more faster than the 30

times later. Especially you can see that when the iteration

is to 50, the change of error rate is very small. According

Figure 1. Parts of samples from JAFFE and our self-built

database.

Figure 2. The pretreatment image and the negative sample.

Table 1. The detection results of the method.

Database Total

sample numberWrong detection

number Correct

detection rate

JAFFE 120 13 89.16%

Self-built

database 42 7 83.33%

Figure 3. The results of seven types of expression recogni-

tion rate.

to the characteristics of this ch ange, without affecting the

recognition rate, we can reduce the number of iterations

to accelerate the classification speed.

5. Conclusions

In the paper, Adaboost algor ithm, which has been succe-

ssfully applied in the field of face detection, applies to

the facial expression classification. We introduce the ba-

sic principle of Adaboost milti-expresion classification

algorithm and demonstrate the process of training and

testing in detail. Because changes of facial expression

mainly exist in eyes and mouth, we treat eyes and mouth

as mutual independent elements, which improved the

speed of training threshold value. The negative samples

is proposed and used in training and testing. The ex-

perimental results demonstrated the feasibility of the

method, which obtained a good recognition result.

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

273

Figure 4. Error recognition rate changing with the iteration

number.

6. Acknowledgements

This work is supported by Department of Electrical and

Information, Government of Handan, China. Authors are

thanking to them for their sponsorship to do this work.

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