Turkish Online Journal of Qualitative Inquiry (TOJQI)
Volume 13, Issue 1, January 2022: 813-822

Experimental Study of Algorithms for Ear Images Distribution in Personal Identity

Turdali Jumayev Saminjonovich
PhD at the Department of “Modern information and communication technologies” of International Islamic Academy of Uzbekistan
e-mail: turdali240483@gmail.com

Mahkamov Anvarjon Abdujabborovich
PhD at the Department of “Modern information and communication technologies” of International Islamic Academy of Uzbekistan
e-mail: mahkamovanvar2020@gmail.com

Tukhtanazarov Dilmurod Solijonovich
PhD at the Department of “Modern information and communication technologies” of International Islamic Academy of Uzbekistan
e-mail: dtuxtanazarov@gmail.com

Dadamuxamedov Alimjon Irgashevich
Department of “Modern information and communication technologies” of International Islamic Academy of Uzbekistan
e-mail: a.dadamuhamedov@iiau.uz

Abstract. This paper presents algorithms experimental study results for distinguishing the ear image features in the matter of identity. As with many biometric technologies, the characteristics distinguishing stage that characterizes a given image is one of the key issues in the individual identification based on the ear image. This is because the program speed and accuracy in identifying an individual based on any biometric technology will depend on the characters that are highlighted. Therefore, in solving the problem, the characteristics characterizing the ear image were distinguished using the discrete cosine replacement, the main component methods, and the fragmentation algorithms. These algorithms can be used in personality recognition systems development based on the ear image.
Keywords: biometrics, recognition, character separation, ear image, discrete cosine substitution method, main component method, eigenvalues, eigenvectors

Introduction. Currently, biometric technologies are widely used, in particular, identity systems based on characteristics such as facial image, pupil, fingerprints, and voice [1-3]. Biometric technologies are widely used in social networks, electronic payments, forensics and a number of other areas. The main task of such computerized systems is the individual identification issue by biometric signs. Although identity recognition systems are currently well developed, some issues require improved accuracy and speed in addressing these systems.
The complete identification scheme combines three identifiers types: portable (cards, keys), secret codes and biometric identifiers. Using a biometric identifier is much more convenient than displaying a card or entering a PIN-code, as portable or hidden identifiers are directly related to the human factor. The card can be lost, the secret code forgotten or given to another person. Biometric identifiers are free from the shortcomings listed above and are stored directly in the individual. Biometric identifiers mainly address the access control and information security issues.
Among biometric technologies, the person identification methods by the ear image play an important role [4-6]. It is known that the identification systems accuracy and reliability on the ear image is high [7-9]. Biometric technology based on identification on this technology has several advantages over other biometric technologies. First, unlike the human face, the ear is virtually unchanged throughout a person’s life and according to his or her mental state. Second, no sophisticated devices other than a simple camera are required to capture an ear image. From the above, it can be concluded that biometric identification systems based on the ear image have good prospects. At present, the identification algorithms development and application on the ear image have not been studied sufficiently in practice. That is why the person identifying issue by the ear image is in the focus of scientists around the world.

Figure 1. Images of biometric objects

The identification system based on biometric objects images consists of the following stages:
•	initial processing of images;
•	distinguish the image characteristics 
•	recognition.
The article aims to develop algorithms for distinguishing the image characteristics based on the biometric objects' images, to assess the algorithms accuracy for the character space formation, which reflects its specific features in the person identification on the ear image.

Problem statement. Suppose we have a set of  images of  individuals:
,
where  are images of the same size and the  -dimensional image is defined in character space.
The main goal is to move from the space of  -dimensional characters to the space of -dimensional characters, i.e. ,
where , .

Problem solving methods. The problem solution involves the of the characteristics separation the ear  image using the discrete cosine substitution method, the main components and partitioning algorithms method, and the accuracy evaluation and the developed algorithms speed.

Switching a color image to a gray image is done in order to reduce the number of data being analyzed by reducing the number of color channels in the image from three (RGB) to one. In this case, this procedure is performed with minimal loss of information about the objects represented in the images.
An algorithm for converting a color image to a gray image has been proposed, which is shown below

where the coefficients were determined using the main component method. The proposed algorithm takes into account the specific nature of the images of biometric objects. 
1. Algorithm for separating the characters characterizing the image of the ear on the basis of the discrete cosine substitution method.
The algorithm for separating the characters characterizing the image of the ear on the basis of the discrete cosine substitution method is implemented in the following steps [9-10]:
Step 1. The image of the earlobe is transferred to the gray image.
Phase 2. M matrix is formed, .
Step 3. D matrix is calculated, , 
where 
Step 4.C is a quantization matrix, i.e. .
Step 5. The elements of the C matrix form a selected vector in the form of a “zig-zag”, i.e. .
Step 6. Coding is done.
Step 7. The characters are separated. 
Step 8. The image is restored.
2. Algorithm for distinguishing the characteristics of the image of the ear on the basis of the method of the main components.
The algorithm for distinguishing the characteristics of the image of the ear on the basis of the method of the main components is carried out in the following steps [11-13]:
Step 1 . The given images are converted to gray images using a color image conversion algorithm. Where , and are the width and height of the images accordingly.
Step 2. The following vector views of gray images are formed  
, ...,,..., 
Where .
Step 3. The "average image" is calculated
.
Step 4. The difference between each image and the "average image" is calculated  
.
Step 5.  matrix is formed,  is its columns, i.e. .
Step 6. The covariance matrix is determined 
.
Step 7. Using the Householder algorithm, the  covariance matrix is reduced to three diagonal matrices.
Step 8. Using the QR algorithm, the eigenvalues  and the eigenvectors  () of the three diagonal matrices are determined.
Step 9. The eigenvalues of  are sorted in descending order: 
.
Step 10. The first  selects the eigenvectors corresponding to the ordered eigenvalues.
Step 11. Each normalized  () image is projected onto the area of the biometric object under consideration
.
Step 12. The resulting  vectors are entered into the database.
Stage 13. Complete.
The complexity of implementing this algorithm is, firstly, the preparation of the given images, and secondly, the determination of the optimal  value.
3. Algorithm for separating statistical signs characterizing the image of the ear on the basis of fragmentation.
The algorithm for the separation of statistical signs characterizing the image of the ear on the basis of fragmentation is carried out in the following stages [14-16]:
Step 1. Forming basic part-sets of data presented in the form of images. The given color image of the biometric object is replaced by the gray image of the dissertation and divided into  pieces consisting of equal parts (Fig. 2). 
 
Figure 2. Images of the ear divided into equal parts
Phase 2. Identify a set of characteristic characters. At this stage, a set of characteristic characters is formed, which are defined in the form of selected moments. The  -order moment of a random value  () is determined as follows:
.
Where ;  –  is a function that returns the value of the  sign for an object.
The central moments are determined as follows

Where
.
Step 3. Separation of parts-sets of strongly connected characteristic characters. At this stage, a system of "independent" parts-sets of characteristics, the composition of which depends on the parameter, is determined. It is possible to have different algorithms by assigning different integer values of this parameter.
The separation of parts-sets of characters with a strong connection is done as follows.  Let   be a set of characters with a strong connection. The  proximity dimension between partitions and  can be determined in different ways, for example:
.
 determined by the following procedure.
Step 1. Part-sets are given in the following form:
.
Step 2. In the view, the elements of the bond matrix are defined.
Step 3. Assimilation is performed: .
Step 4. The largest element of the matrix is determined .
Step 5. , condition is checked. If this is done, then and are combined and a new bond matrix is formed.
Step 6. condition is checked. If it is done, then go to step 7, otherwise  and the transition to step 4 is complete.
Step 7. Complete.
Step 4. Identify the “unrelated” character in each part-set of strongly linked characters. At this stage, different methods can be used to select unrelated (representative) characters from subsets of characters that have a strong connection. The basic idea of selection is to select the maximum set of “independent” (or weakly connected) characters.
 is subsets of characters with a strong connection. Assume that is the number (capacity) of the elements of the set:
, .
In this case, this step can be performed as follows. 
Step 1. Assimilation is performed: , , , , , .
Step 2.  condition is checked. If it is done, then go to step 9.
Step 3.  condition is checked. If it is done, then so be it , .
Step 4.  condition is checked. If it is done, then go to step 8.
Step 5. Proximity estimates are calculated .
Step 6.  is determined.
Step 7. Assimilation is done:, .
Step 8. Assimilation is performed and go to step 2.
Step 9. Assimilation is done:.
Step 10.  condition is checked. Агар if it is done, then proceed to step 13.
Step 11. ва proximity estimates are calculated.
Step 12.  condition is checked. If it is done, , otherwise .
Step 13. Assimilation is performed:.
Step 14.  condition is checked.If it is fulfilled, then go to step 10.
Step 15. .
Step 16. Complete
As a result of the implementation of the above steps, a subset of  characters () is formed, each of which represents a separate subset of characters with a strong connection. The isolated marks of a biometric object are used to solve the problem of identification.

 4. Recognition algorithm. identification based on the algorithm of close neighbors is performed in the following 3 steps.
In step 1, the proximity between the unknown image S and each image belonging to the study selection is . 
In Step 2, the proximity between the given image and the image class is determined. To determine the similarity between a given image and an image class, the number of images that are separated in the first step belongs to each  image class .
Suppose we have in class , in class , ..., in class , where is the relationship between the unknown S image and the class of  images as follows: 
.
In step 3, the decision is made. S with an unknown image  proximity measurements between image classes is  the decision-making procedure is as follows: 
If ,  otherwise .
A block diagram of personality recognition based on an image of the ear is shown in Figure 3.

Preliminary processing the ear image

Find the area where the ear is located in the image

Distinguish the identification marks in the ear image

Begin

Insert an image

Identify a person based on the ear image

Complete

Figure 3. Block diagram of identity based on the ear image

Experimental research results. To solve practical problems using the above algorithms, a program for separating the ear image signs in the Delphi programming environment was developed. In order to test this program functionality, the characteristics distinguishing issue of the ear images of 40 people was resolved. At the same time, 20 ear images were taken for each class.
The selected characters effectiveness was assessed based on objects quality recognition assessment based on these characters. In other words, the higher the results of solving the recognition problem, the higher the effectiveness of the selected characters.
1) The main results obtained from the algorithm for distinguishing the ear image characteristics on the basis of the discrete cosine substitution method are given below: 
•	when the best result in this algorithm compression coefficient is  ,the recognition accuracy was 86% (Figure 4);
•	the algorithm operating speed has significantly improved after the 3rd stage change implementation;
•	due to the large character’s number separated on the basis of discrete cosine substitution, the time required for recognition was increased.

Figure 4. The recognition result using discrete characters based on discrete cosine substitution.
2) The main results obtained from the algorithm for distinguishing the ear image characteristics on the basis of the main method components are as follows:
•	all eigenvalues are sorted in descending order, and as an experimental studies result, the largest 13 eigenvalues are isolated and eigenvectors corresponding to these eigenvalues are formed. The specific vectors formed were considered as the characteristics that characterize the image. These characters’ efficacy was assessed and the accuracy rate was 89% (Figure 5);
•	Recognition accuracy based on ordered eigenvalues did not differ significantly when comparing the eigenvalues when all eigenvalues were obtained.

Figure 5. The identification result using symbols separated on the basis of the main component method.
3) The algorithm results for separating the statistical features that characterize the ear image on the fragmentation basis are given below:
•	the best result in this method - when the pieces number was 12, the recognition accuracy was 90% (Figure 6);
•	very little time was spent on identification due to the small number of characters allocated;
•	the higher the images number in each class in this algorithm, the better the accuracy level.

Figure 6. The recognition result using statistical symbols characterizing the ear image on the fragmentation basis.
Conclusion. The image recognition accuracy and speed depends on the characteristics separation that characterize the image. The characters recognizing problem that characterize the image is that the increase in the character’s number that characterize the image affects the algorithm speed. It is therefore necessary to distinguish the characters that characterize the image well, i.e. the representative. From the research results, it can be concluded that the algorithms based on the head components method and statistical indicators calculation of the ear image gave better results than the method based on discrete cosine displacement. This is because in a discrete cosine shift method, the number of characters is greater than the other two algorithms. This, as mentioned above, has its own effect on the accuracy and processing speed of the algorithm. Discrete cosine shift can be used effectively in compression of images.
References:
[1]	G.A. Kukharev Biometric systems: Methods and tools for identifying a person's personality. SPb.: Polytechnic, 2001.240 p.
[2]	Burge M. and Burger W. (1998a) Ear Biometrics. BIOMETRICS: Personal Identification in a Networked Society, p 273-286. Kluwer Academik.
[3]	Fazilov Sh.Kh., Mirzaev N.M., Djumaev T.S. Formation of a set of representative features of images of a face and ears in problems of personality identification//Proceedings. Russian Scientific and Technical Society of Radio Engineering, Electronics and Communications named after A.S. Popova//12th International Conference and Exhibition // Reports // Series: Digital Signal Processing and Its Application Issue: XII-2: pp. 62-65. DSPA-2010. Moscow, Russia
[4]	Fazilov, S. X., Mahkamov, A. A., & Jumayev, T. S. (2018). Algorithm for extraction of identification features in ear recognition. In Informatics: problems, methodology, technologies (pp. 3-7).
[5]	FazilovSh.X., MahkamovA.A., JumayevT.S. Algorithm for extraction of identification features in ear recognition // Informatics: problems, methodology, technologies: Materials of the XVII international scientific and methodological conference. Voronezh, 2018.Vol. 2 - p. 3-7. p.
[6]	Mirzaev O.N., Djumaev T.S. Construction of personality recognition algorithms based on the image of the ears // Current state and prospects for the use of information technologies in management: Reports of the Republican Scientific and Technical Conference. September 7-8, 2015. CRPP and agro-industrial complex at TUIT, 342-348 p.
[7]	Jumaev T.S. Algorithm for distinguishing the characteristics of the image of the ear on the basis of discrete cosine displacement // TATU messages. - Tashkent, 2011. -№2. 74-78 p.
[8]	Mirzaev, N.M., Radjabov, S.S., & Djumaev, T.S. (2008). On the parameterization of models of recognition algorithms based on the assessment of the interrelation of features. Informatics and energy problems, (2-3), 23-27.
[9]	Jumayev, T. S., Mirzayev, N. S., & Makhkamov, A. S. (2015). Algorithms for segmentation of color images based on the allocation of strongly coupled elements. Studies of technical sciences, (4), 22-27.
[10]	Mirzayev, N. M., S. S. Radjabov, and T. S. Zhumayev. "O parametrizatsii modeley algoritmov raspoznavaniya, osnovannyh na otsenke vzaimosvyazannosti priznakov." Problemy informatiki i energetiki (2008): 2-3.
[11]	Mirzayev N. M., Radjabov S. S., Jumaev T. S. Isolation of characteristic features of facial images in personality recognition problems. Neurocomputers and their application.-2016.
[12]	Фазылов Ш. Х., Мирзаев Н. М., Махкамов А. А. Выделение геометрических признаков изображений ушных раковин //XI всероссийская научная конференция «нейрокомпьютеры и их применение. – 2013. – Т. 19.
[13]	JUMAEV TURDALI SAMINJONOVICH, & MAHKAMOV ANVARJON ABDUJABBOROVICH. (2021). ALGORITHM FOR EXTRACTION OF IDENTIFICATION FEATURES IN EAR RECOGNITION. International Journal of Innovations in Engineering Research and Technology, 7(05), 216–220.
[14]	Abdujabborovich M. A. et al. Human personal identification algorithms from the image of the ear //International Engineering Journal For Research & Development. – 2020. – Т. 5. – №. 6. – С. 5-5.
[15]	Abdujabborovich M. A. ONE WAY TO IDENTIFY A PERSON BASED ON THEIR IMAGE IS TO PROVIDE SECURITY //International Engineering Journal For Research & Development. – 2020. – Т. 5. – №. Special Issue. – С. 8-8.
[16]	Фазылов Ш. Х., Мирзаев Н. М., Махкамов А. А. Выделение геометрических признаков изображений ушных раковин //XI всероссийская научная конференция «нейрокомпьютеры и их применение. – 2013. – Т. 19.