Academic Journal of Science and Technology 
ISSN: 2771-3032 | Vol. 1, No. 3, 2022 

 

6 

Comparing SVM with BP to Identify the Young Females' 
Body Type 
Suyu Zhang1, * 
1College of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China 
* Corresponding author: SuYu Zhang (Email: 23209695@qq.com) 

 
Abstract: Nowadays, from mass production to mass customization has occupying the domain of apparel industry. And new 
techniques that provide fitting apparel are needed to satisfy individual demands. In the paper, a math arithmetic using 
SVM(Support Vector Machine) is introduced. Firstly, a new strategy of dependency analysis is proposed. Secondly, the paper 
compares the approach ability of SVM and BP. Finally, some chosen feature types are used for learning and for creating the 
modification matrix using SVM and BP. Two model is established to identify body type. The result, which realizes auto-mapping 
from body data to specific estimation, has increase accuracy highly by using SVM. 

Keywords: Match conversion, Pattern conversion, BP, SVM. 
 

1. Introduction 
The introduction should provide background information 

(including relevant references) and should indicate the 
purpose of the manuscript. Cite relevant work by others, 
including research outside your company. Place your work in 
perspective by referring to other research papers. Inclusion of 
statements at the end of the introduction regarding the 
organization of the manuscript can be helpful to the reader.By 
far, customers are longing for customized garments and 
individualized garments that fit for their body shape and 
satisfy their aesthetic needs. However, there are limited 
information about the body shape, most of which are the 
length and circumference measurements. At the same time, 
there are few differences in size label. Therefore, who are lack 
of apparel knowledge, or haven’t “standard” body shape, need 
to spend a lot of time and vigor tying on. The developmental 
trend of garment industry is fashion and customization, and 
the require for comfort and high quality makes MTM(Made 
to Measure) a necessity, which make it possible to apply 
MTM in garment industry [1]. Fortunately, new 
manufacturing technologies such as 3D body scanners, 
CAD/CAM, have begun to play a key role in increasing the 
effectiveness, flexibility, and precision of production [2]. 

Body type analysis plays an important role. It is not only 
one of the key technologies to realize MTM, but also an 
important procedure to combine 3D body scanner with digital 
garment production. Body type analysis means to analyze the 
data in Anthropometrical Database, then acquire parameters 
by computing the data based on the statistical theories, finally 
classify human body characteristics using clustering analysis. 
It provides advantageous help to the mapping from body form 
to clothing size and other application of MTM [3]. 

But the description of body character involves the 
relationship between data and specific judgment, which is 
hard to depict by traditional mathematic methods because of 
the non-linear relation among parameters and the existence of 
multi influencing factors. So we adopt SVM which is the very 
method to resolve this kind of problems. By checking the 
model through actual body measurements, the method in this 
paper can fully satisfy the need of body type analysis in MTM. 

The rest of the paper organized as follows: Section 2 

describes the general structure of the SVM model; Section 3 
depicts the BP network model in brief; Section 4 explains the 
data source and classification, contrast the results between 
SVM and BP; finally some concluding remarks are drawn 
from section 5. 

2. Theory and Arithmetic of SVM 
SVM take the following advantages over conventional 

statistical learning algorithms: (1) High generalization 
performance even with high dimension feature vectors; (2) 
The ability to manage kernel functions that map input data to 
higher dimensional space without increasing computational 
complexity. 

2.1. Optimal Hyperplane 
The main idea of SVM is to create an optimal hyperplane 

to classify the data into two classes (positive and negative) 
and maximize distance between the hyperplane separating the 
two classes and the closest data points to the 
hyperplane.(Figure 1)[4] 

 

 
Figure 1. Optimal classifying line under linear classified 

condition 
 

Figure 1. shows training examples linearly separated into 
two classes. 

It starts with a set of l training data (x1,y1), ,(xl.yl), where 
n

ix R is an n-dimensiona vector and { 1, 1}iy is the 
class label of its data: 

f(x)=-1 

f(x)=0 

Optimal hyperplane 
h l

Margin=2/||w|
|Origin 

f(x)=1 



 

7 

 1 1( , ),x y Rn R 
The basic idea of SVM is to maximize the margin between 

the positive and negative examples. Firstly, the training 
examples are mapped from original spaces Rn to feature 
spaces 1 2( ) ( ( ), ( ), ( ))x x x x   by a non-linear 
function (  ) Secondly, non-linear estimated function is 
changed into linear estimated function in hyperplane feature 
spaces by the most optimal decision function, as follows: 

 
 ( ) ( )y x w x b  
 
Finding w and b is just minimizing R: 
 

2 *

1

1min ( , ) || || ( )
2

R w w c      (1) 

 
s.t. i i i( )y w x b  

  * *( ) , , 0, 1,i i i i iw x b y i l  
in Eq(1): ||w|| Controlling the complication of models 
        C  Const 
       Misclassification errors 
The answer of Eq(1) can be got by Lagrang Function: 
 

  

* * * *

1

*

1 1

* * *

1

1( , , , , , , , ) ( ) -
2

(( ( )) - ) - ( -

( ( )) - ) - ( )

l

i i
i

l l

i i i i i i
i i

l

i i i i i i
i

L w c w w c

a w j x y b y

w j x b

 (2) 

 
According to optimal conditions: 

*0, 0, 0, 0
i i

L L L L
w b

 

The results are : 
 

* *

1 1

* *

( ) ( ), ( ) 0,

0, 0, 1,

l l

i i i i i
i i

i i i i

w x

c c i l
         (3) 

 
Defining the Kernel Function based on Mercer’s conditions: 
 

( , ) ( ) ( )i j i jk x x x x                     (4) 
 
Through Eq(2),Eq(3) and Eq(4), the optimization problem 

is exchanged as follows:
 

 
* *

, 1

* *

1 1

1max *)=- ( )( ) ( , )
2

( ) ( )

l

i i j j i j
i j

l l

i i i i i
i i

W K x x

y

   (5) 

 

s.t.  
*

*

( ) 0

0 ,0 , 1,
i i

i ic c i
 

So the final non-linear estimated function is: 
 

   *( ) ( ) ( , )i j i jf x K x x b               (6) 

 

2.2. Kernel Function 
We choose different Kernel functions that can construct 

variable support vector products. In general, Kernel function 
including: 

1)Polynomial Kernel Function: ( , ) [( , ) 1]d
i j i jK x x x x ; 

2)Radial Machine Kernel Function:
2

2

|| ||

2( , )
i jx x

i jK x x e ; 
3)Neural Network Kernel Function: 

( , ) ( ( , ) )i j i jK x x S v x x c  
In this paper, we use radial machine Kernel functions that 

have been very effective. 

2.3. SVM Network Structure 
The realization of SVM in this paper due to the structure as 

Figure 2 shows: 

 
 

Figure 2. SVM network structure diagram 
 

Where αi α*i is the weight of network, variable 
x1,x2,…,xl is introduced for input numbers and y is output 
number. 

Systematic identification based on SVM steps as follows: 
1)Choosing the training examples, modifying and pre-

computing the data; 
2)Using the function of SVM to train the chosen data: input a 

set of l training data x1,x2,…,xl, then output variable y;  
3)Constructing the modeling of SVM and predicting the 

unknown examples.  

3. Artificial Neural Network 
Artificial neural network BP is made up of forward 

propagate and back propagation two parts, its structure as 
Figure 3 shows. 

   
 

Figure 3. Structure of BP network 

  

x1 x2 x

y 

 



 

8 

 
The output y can be obtained by the equation: 
 

bwpfa                (7) 

 
Variable w and b are decided through the produce of training. 

Otherwise, we use logical sigmoid function as transmitting 
function: 
 

            xe
xsiga

1
1           (8) 

 
There is one layer or more layers besides the inputting node 

in the network, the same layer nodes do not have any coupling. 
That means the network with deviation and at least one S type 
imply layer adding one linear output layer can approach any 
rational function [5], [6].  

4. Experiment 
4.1. Analysis on Body Characteristic Parts  

According to garment pattern design and the experience of 
fashion designers, there are more th n 80 geometric 
characteristic sizes [7], [8]. Among them the following four 

parts, flank shape, bust shape, hips shape and abdomen shape 
can reflect the most of the information about the body form, 
and are also the most important geometric characters of 
human body model [9], [10]. 
  The flank part can reflect information which the front part 
and back part can not reflect. For example, vertical section 
body form curve can clearly reflect whether the body is 
prominent bust, bend back or not. Bust and back will 
influence the length of front centerline and back length, and 
then result in the changes of patterns. 

The breast is the main factor influencing bust shape. Its size, 
shape and height directly affect breast dart design of the front 
piece.  

The hip part is an important part in the research on the 
fitting of the lower part. The angle from back waist to back 
hips is a parameter to design back rise. It can reflect the raise 
shape of hips, the fuller the hips, the larger the angle, and the 
thinner the hips, the smaller the angle. 

The abdomen part influences the waist dart. It is shown that 
the abdomen shape plays a more important part in  body 
form. It is easy to accumulate fat for abdomen, which will 
cause the change of the front of the lower part [11]. 

Summing up, we get the parameters depicting women’s 
body form as Table 1 shows. And this demonstrates the multi-
dimension of human body. 

 
Table 1. Parameters and Description of the Parts 

Parts Parameters Description 

Flank 
back bend index, the angle from shoulder blade to back 

neck, the angle from shoulder blade to back waist, Under 
bust slope angle, Front neck to breast angle 

back bend(C1) 
medium(C2) 

bust prominent (C3) 

Bust 
under bust slope angle, front neck to breast angle, side neck 

to breast/ breast to waist, bust to bust /bust front, bust 
width/bust thickness, bust full 

flat(R1) 
medium (R2) 

full(R3) 

Hip The angle from back waist to back hips, hips full, 
hips thickness/hips length 

flat (T1) 
medium (T2) 

full (T3) 

Abdomen the angle of abdomen prominence, waist full, abdomen full, 
waist length 

medium(F1) 
prominent (F2) 

4.2. Body Type Identification Method Based on 
SVM Algorithm 

Because garments enterprises haven’t analyzed the target’s 
body form, they always produce “safe” garments, which 
results that customers can not buy garments fitting their body 
form. Fully analysis on body form enables people to acquire 
more information when they are tailoring and transfer 
sensibility recognition to logos evaluation. This rationalizes 
the formulation of data. Therefore, if we can simulate and 
reconstruct the fashion designers’ experience and arrange the 
proportion of every body form by computer, the designers’ 
burden will be lessened. As the SVM is able to approximate 
the complicated non-linear relation of human body and 
simulate designers’ non-linear experience. In this way, we can 
determine the system category to be developed and garment 
production using the results of body form analysis. 

In this research, four machines are constituted on the basis 
of flank shape, bust shape, hips shape and abdomen shape and 
their optimal structures are acquired. we take the 
classification of flank shape as an example. 

Ascertain the collection of training samples: In this 
experiment, we choose 350 young women age from 18 to 35, 
average height 160.55cm, average weight 50.5kg and average 
age 23. Equipment: [TC]2 3D body scanner made in American, 
the revised altimeter and weight scale and so on. First, the 350 
young women’s bodies are scanned by 3D body scanner. Then, 
10 experts score the parts. The data of the 350 individuals’ 
bodies are made as input and the corresponding description as 
output, so the training sample collection is got.   

2) Pre-computing the data: Based on the analysis on human 
body form character and fully considering the need of 
garment pattern design, e can use the parameters which in 
Tab.1 as the input of the SVM, they are: back bend index, the 
angle from shoulder blade to back neck, the angle from 
shoulder blade to back waist, Under bust slope angle, Front 
neck to breast angle. 10 experts are asked to give semantic 
description of the body form and their judgments “back 
bend ”, “medium” and “bust prominent” (the “description” in 
Tab.1) are regarded as output vectors. 

The data are normalized in [-1 ,1].The normalization 
formula is  



 

9 

min
2 1

max min
i i

i
i i

x x
x

x x
         (9) 

 

ix  the original data of index variables.  

max { ix }  the maximum of the same index data. 

min { ix } the minimum of the same index data.). 
3) Constructing the modeling of SVM: In the process of 

training, there are 5 parameters of input and 3 categories of 
output in every training sample, which are 1, 2, 3. ccordingly, 
their goal outputs are [+1,-1,-1], [-1,+1,-1], [-1,-1,+1]. And 
we make the category corresponding to the maximal output 
as the category for classifying. Finally, 300 of young 
women’s bodies are regarded as training samples and 50 as 

testing samples. 

4.3. Comparing SVM and BP 
By testing every parameter, we know when Learning ration 

(Lr)=0.05, the maximal iterative steps (epochs)=100000, goal 
precision (goal)=le-2, the error index get to its optimization. 
Therefore, the model structure of Flank shape is 5:3. This 
structure refers to there are 5 input parameters, and 3 output 
data. 

According to the above method, there are 350 samples 
every single part. 300 of them are made as training samples 
and 50 of them as testing samples. After repeated tests, we 
select the models whose structures fit the producing in 
clothing factories most, and the models have been applied to 
garment producing and the result shows the model works well. 
The comparison between SVM and BP see Table 2.[12] 

 
Table 2. The comparison between SVM and BP 

parts structure 
Success rate in training sample (%) Success rate in 

testing sample (%) 
SVM BP SVM BP 

Flank 5: 3 99.1 97.9 98.5 85.7 
Bust 6: 3 99.6 97.6 99.3 92.0 
Hip 3: 3 99.4 98.7 99.4 92.0 

Abdomen 4: 2 99.7 98.2 98.8 91.9 
 

From Table 2 we can see clearly that the average precisions 
of training sample are above 99  by using SVM algorithm 
which also indicates fine prediction. It is better than the BP 
that Former researchers provided. Besides, the following two 
aspects may cause the errors: First, the 3D body scanner often 
makes mistakes when looking for the neck back, and the flank 
shape is reflected by Neck to Waist-Front and Neck to Waist-
Back, these cause the input samples incorrect relatively. 
Second, the training samples are not adequate.  

5. Conclusions and Future Works 
5.1. Conclusions 

) The research in this paper demonstrates the feasibility of 
SVM applied to body type classification and prediction. The 
more important is that the models of SVM take superior over 
BP. It can avoid the partial minimal point and improve the 
precision  of results. The models have been applied to 
garment producing and the result shows the models have a 
good effect on the garment producing.  

2) As long as we find out the factors influencing figure 
characteristic and enough samples to be learned by the 
network, we can make accurate and applied evaluation on 
body type, and optimize the index. This is significant to 
garment enterprises because it provides them with basis to 
arrange producing garment size reasonably. 

3) This paper provides powerful data support to the 
mapping between body form and garment size and solves one 
of the key technologies to realize MTM. The body type 
analysis algorithm in this paper satisfies the need of body type 
analysis; it will promote the realization of MTM in our 
country. 

5.2. Future Works 
First, we can make software to realize the identify methods. 

Second, more samples are need to make the model have more 

extensive applications and to reduce errors. Third, we can 
combine SVM with BP, or study on the parameter setting 
further.  

Acknowledgment 
The paper is supported by Zhejiang Provincial Department 

of Education named Design and Research of Apparel Pattern 
Recognition and Pattern Conversion System(Y202045071). 
The authors would like to thank the 10 experts for their 
contributions to this project and the former researchers. 

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