نشریه علمی پژوهشی طب انتظامی Journal of Police Medicine
Volume 11, Issue 1 (2022)
J Police Med 2022, 11(1) |
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Babaei M, Shirzad J, Keshavarz Meshkin Pham K, Faghih Fard P, eftekhari A. Challenges of Using Biometric Evidence in Identification. J Police Med 2022; 11 (1) : e29
URL: http://jpmed.ir/article-1-1100-en.html
URL: http://jpmed.ir/article-1-1100-en.html
Mohsen Babaei1 
, Jalal Shirzad *2, Kamran Keshavarz Meshkin Pham1, Peyman Faghih Fard1, Asghar Eftekhari3
, Jalal Shirzad *2, Kamran Keshavarz Meshkin Pham1, Peyman Faghih Fard1, Asghar Eftekhari3
1- Department of Identity Recognition & Medical Sciences, Faculty of Intelligence & Criminal Investigation Science & Technology, Amin Police University, Tehran, Iran
2- Department of Identity Recognition & Medical Sciences, Faculty of Intelligence & Criminal Investigation Science & Technology, Amin Police University, Tehran, Iran , jalal_shirzad@yahoo.com
3- Department of Anti Narcotic, Faculty of Intelligence & Criminal Investigation Science & Technology, Amin Police University, Tehran, Iran
2- Department of Identity Recognition & Medical Sciences, Faculty of Intelligence & Criminal Investigation Science & Technology, Amin Police University, Tehran, Iran , jalal_shirzad@yahoo.com
3- Department of Anti Narcotic, Faculty of Intelligence & Criminal Investigation Science & Technology, Amin Police University, Tehran, Iran
English Extended Abstract: (1211 Views)
INTRODUCTION
... [1]. Recognizing people traditionally based on identifying them using their physical characteristics (fingerprints, iris, face, way of walking, and DNA) or their behavioral characteristics is called the biometric method [2, 3]. Face recognition is a common way to identify people using their facial features. However, like other biometric methods, in environments with restrictions, the results' quality decreases significantly [4, 5]. ... [6]. A biometric system is a pattern recognition system. A simple biometric system has four essential parts [7]: 1- sensor block (receiving biometric information), 2- feature extraction block (feature vector extraction), 3- comparison block (comparing the vector with templates), 4- decision block ( Identification). ... [8-10]. Although biometric markers are widely used in developed countries today, due to the complexity of hardware and software, extraction of biometric markers such as DNA profile [11], fingerprint characteristics [12], facial characteristics [13], iris and voice characteristics [12], and also the costs of their construction and commissioning face specific challenges in developing countries [14-16]. Challenges that are effective in using biometric evidence: 1- Challenges of providing organizational financial resources, 2- Challenges of providing expert human resources, 3- Challenges of training human resources. The current research investigates the challenges of using biometric evidence in identity detection. Among the research conducted in this field, Garud and Agrawal's research can be mentioned as one that sought to solve the problem of fake faces in recognition technology. Their suggested solutions are blinking, counting based on movement, microtexture extraction, Fourier spectrum analysis, element descriptors, and face recognition focusing on the forehead and image backgrounds [17]. Also, Nogueira, Alencar Lotufo, and Machado have used complex neural networks in their research to detect fingerprint biometrics, which shows that if these networks are trained in advance, they can obtain new results without the need for extensive parameters and expensive designs. Also, this method is highly accurate on minimal training sets [18]. In another study, Andrey 2020 states that in the context of expanding the tools of credit institutions related to the management of the risk of involvement in suspicious transactions, there is a need to introduce new technologies to combat money laundering and the financing of terrorism. Biometric identification technologies are effective in minimizing risk and protecting corporate information systems of banks. ... [19].
AIM(S)
This study aimed to investigate the challenges of using biometric evidence in identification.
RESEARCH TYPE
The current research is a descriptive survey type and method, applied in terms of purpose and nature, and a documentary survey in data collection.
RESEARCH SOCIETY, PLACE & TIME
Questionnaires were distributed and collected in the winter of 2021. The detectives and investigation police officers of Gilan province in Iran were considered the statistical population with 109 people.
SAMPLING METHOD AND NUMBER
Considering the number of the statistical population, the total population was considered in this research (109 people). The criterion for entering the study was at least five years of work experience, and the exclusion criterion from the study was an unwillingness to continue attending.
USED DEVICES & MATERIALS
A researcher-made questionnaire was used as a research tool (Table 1). The scale was used to measure the biometric evidence using seven questions. They measured the challenges of providing organizational financial resources using five questions and the challenges of providing expert human resources using seven questions. Finally, five questions were compiled to measure the challenges of training human resources, and their validity and reliability were checked. The questions were closed-ended and scaled based on a five-point Likert scale from shallow (5 points) to very much (1 point).
ETHICAL PERMISSION
Ethical considerations were observed in conducting this research, including confidentiality of questionnaires, informed consent, and voluntary withdrawal of participants from the research.
STATISTICAL ANALYSIS
For data analysis, Pearson's R, t-test, and one-way analysis of variance were used with SmartPLS version 3 software. A confidence factor of 95% and an error level of 5% were considered for all routes.
FINDING by TEXT
In this study, 84 detectives and police officers of Gilan province in Iran (14 women and 70 men) had completed the questionnaires correctly; 23 of them had a high school diploma, 19 had an associate degree, 38 had a bachelor's degree, and 4 had a master's degree. By performing the Kolmogorov-Smirnov test on the data distribution (variables of providing organizational financial resources, providing expert human resources, and training human resources according to the respondents), the p-value was more significant than 0.05, which indicated the normal distribution of the data. In the questionnaire examination, the factor loading of all items was above 0.4, which confirmed the internal correlation. The average value of the extracted variance of the variables was higher than 0.5, and the convergence validity of the measured variables was favorable. Composite reliability was obtained as more significant than the average variance extracted, which was reported due to the convergence validity of desired measurement models. To check the discriminated validity or divergence, the average value of the extracted variance was more significant than the values of the squared covariance and the maximum squared covariance. The internal reliability of the tools was also confirmed (Table 2). According to Table 3, since the mean square of the extracted variance of all variables was higher than the correlation of the constructs with other constructs in the model, the existence of discriminated validity among the research variables was confirmed, and it showed that the measurement tool had a suitable validity. The impact of the challenges of using biometric evidence in identification was obtained using standard coefficient parameters and significant numbers with SmartPLS software (Figures 1 and 2). The impact and t value of the variable of providing organizational financial resources is 0.817 and 12.78, and the provision of expert human resources was respectively 0.658 and 9.24. Also, the human resources training was obtained as 0.508 and 27.6 in using biometric evidence in identification (Table 4). The t values of the three components showed their confirmation. To investigate the impact of three variables of providing organizational financial resources, providing expert human resources, and the challenges of training human resources in using biometric evidence in identification, a one-sample t-test was used, and the results are presented in Table 4. The value of the t statistic for all variables was more significant than the critical value of 1.96, so all the variables of this research were in good condition. The result of calculating Spearman's correlation coefficient (Spearman's rho) to investigate the relationship between biometric evidence and human resources training was 0.482, and the significance level was p<0.001. Also, the Spearman correlation coefficient for examining the relationship between the use of biometric evidence and the allocation of financial resources was 0.353, and the lack of human resources was 0.519 (p<0.001). Therefore, with 95% certainty, there was a significant relationship between using biometric evidence and human resource training, financial resource allocation, and lack of human resources. The results of the Friedman test to rank the averages showed that the opinions of the respondents were as follows; the variables of human resources training with an average rating of 1.35, allocation of human resources with an average rating of 2.46, and lack of human resources with an average rating of 2.19. Considering that the calculated significance level was lower than the intended significance level (p-value=0.05), it can be said that there was a significant difference between the variables in terms of ranking, and the rank of the variable of human resource allocation was higher than other variables.
MAIN COMPARISON to the SIMILAR STUDIES
According to Figure 2, the research population and the challenges of providing financial resources had the most significant impact on the use of biometric evidence. In other words, in the current situation, using biometric evidence and new methods and tools requires financing and investment in this sector, indicating a need for more attention on the part of the authorities. These results are consistent with the research conducted by Andrey in 2020 [19] and Pari and Hamidi [20]. Also, the challenge of providing expert human resources had a more significant impact on the use of biometric evidence than the challenge of training expert human resources. The use of expert forces in the analysis of laboratory samples (such as the use of biometric evidence in identity authentication) leads to the improvement of correct and accurate evaluation results, which is consistent with the research conducted by Shirzad et al. [21], Zohreh Nedayee [22] as well as Ebrahimi and Sadeghinejad [23]. The results of this research indicated that there needs to be more expert human resources. This issue originates from the need for more recruitment of expert personnel in recent years or the lack of scientific improvement of the existing personnel. The third challenge in this research was training the human forces employed in using biometric evidence in identity authentication, which was a lower priority than the two challenges of providing financial resources and allocating expert personnel. Cultivating skilled people, referred to as human resource development, is an inevitable necessity for organizations to survive and progress in today's ever-changing world.
For this reason, training is considered one of the main tasks of human resources management and is always considered an essential factor in formulating development plans or organizational changes. In any case, simply providing training in the form of courses and training programs cannot be an influential factor in improving human resources in the organization. The research by Nasiri in this field also shows the importance of training [24].
LIMITATIONS
The limitations of this research were the non-cooperation of some detectives and police officers of the province, despite sufficient explanations by the researchers. As a result, they did not participate in this research and did not complete the questionnaire.
SUGGESTIONS
It is suggested to increase this department's budget due to the urgent need for identification methods and the increase in security sensitivities. It is also suggested to provide and reduce the costs by exploiting private investment departments.
CONCLUSIONS
Based on the research, the lack of adequate financial resources plays a significant role in using biometric evidence for identification. Also, the variable of providing expert human resources and training employed human resources are in the following steps. The use of biometric evidence and the use of new methods and tools require financing and investment, which indicates a lack of attention on the part of the authorities, and the lack of expert human resources can originate from the lack of recruitment of specialists in recent years or the lack of scientific improvement of the existing forces. Also, providing training in the form of courses and training programs cannot be an influential factor in improving human resources in the organization.
ACKNOWLEDGMENTS
We appreciate all the detectives, intelligence officers, and officials of Gilan province in Iran who cooperated in conducting this research.
CONFLICT of INTEREST
The authors state that the present study has no conflict of interest.
FUNDING SOURCES
The present study did not have any financial support.
Table 2) Reliability table of measurement tools
Table 3) Correlation matrix of variables
Figure 1) The relation between the challenges of providing organizational financial resources and expert
human resources and their training with the use of biometric evidence and the use of the parameter of standard coefficients.
Figure 2) The relationship between the challenges of providing organizational financial resources, providing expert
human resources and training human resources with the use of biometric evidence using the parameter of significance.
Table 4) One-sample mean t test of research variables
... [1]. Recognizing people traditionally based on identifying them using their physical characteristics (fingerprints, iris, face, way of walking, and DNA) or their behavioral characteristics is called the biometric method [2, 3]. Face recognition is a common way to identify people using their facial features. However, like other biometric methods, in environments with restrictions, the results' quality decreases significantly [4, 5]. ... [6]. A biometric system is a pattern recognition system. A simple biometric system has four essential parts [7]: 1- sensor block (receiving biometric information), 2- feature extraction block (feature vector extraction), 3- comparison block (comparing the vector with templates), 4- decision block ( Identification). ... [8-10]. Although biometric markers are widely used in developed countries today, due to the complexity of hardware and software, extraction of biometric markers such as DNA profile [11], fingerprint characteristics [12], facial characteristics [13], iris and voice characteristics [12], and also the costs of their construction and commissioning face specific challenges in developing countries [14-16]. Challenges that are effective in using biometric evidence: 1- Challenges of providing organizational financial resources, 2- Challenges of providing expert human resources, 3- Challenges of training human resources. The current research investigates the challenges of using biometric evidence in identity detection. Among the research conducted in this field, Garud and Agrawal's research can be mentioned as one that sought to solve the problem of fake faces in recognition technology. Their suggested solutions are blinking, counting based on movement, microtexture extraction, Fourier spectrum analysis, element descriptors, and face recognition focusing on the forehead and image backgrounds [17]. Also, Nogueira, Alencar Lotufo, and Machado have used complex neural networks in their research to detect fingerprint biometrics, which shows that if these networks are trained in advance, they can obtain new results without the need for extensive parameters and expensive designs. Also, this method is highly accurate on minimal training sets [18]. In another study, Andrey 2020 states that in the context of expanding the tools of credit institutions related to the management of the risk of involvement in suspicious transactions, there is a need to introduce new technologies to combat money laundering and the financing of terrorism. Biometric identification technologies are effective in minimizing risk and protecting corporate information systems of banks. ... [19].
AIM(S)
This study aimed to investigate the challenges of using biometric evidence in identification.
RESEARCH TYPE
The current research is a descriptive survey type and method, applied in terms of purpose and nature, and a documentary survey in data collection.
RESEARCH SOCIETY, PLACE & TIME
Questionnaires were distributed and collected in the winter of 2021. The detectives and investigation police officers of Gilan province in Iran were considered the statistical population with 109 people.
SAMPLING METHOD AND NUMBER
Considering the number of the statistical population, the total population was considered in this research (109 people). The criterion for entering the study was at least five years of work experience, and the exclusion criterion from the study was an unwillingness to continue attending.
USED DEVICES & MATERIALS
A researcher-made questionnaire was used as a research tool (Table 1). The scale was used to measure the biometric evidence using seven questions. They measured the challenges of providing organizational financial resources using five questions and the challenges of providing expert human resources using seven questions. Finally, five questions were compiled to measure the challenges of training human resources, and their validity and reliability were checked. The questions were closed-ended and scaled based on a five-point Likert scale from shallow (5 points) to very much (1 point).
ETHICAL PERMISSION
Ethical considerations were observed in conducting this research, including confidentiality of questionnaires, informed consent, and voluntary withdrawal of participants from the research.
STATISTICAL ANALYSIS
For data analysis, Pearson's R, t-test, and one-way analysis of variance were used with SmartPLS version 3 software. A confidence factor of 95% and an error level of 5% were considered for all routes.
FINDING by TEXT
In this study, 84 detectives and police officers of Gilan province in Iran (14 women and 70 men) had completed the questionnaires correctly; 23 of them had a high school diploma, 19 had an associate degree, 38 had a bachelor's degree, and 4 had a master's degree. By performing the Kolmogorov-Smirnov test on the data distribution (variables of providing organizational financial resources, providing expert human resources, and training human resources according to the respondents), the p-value was more significant than 0.05, which indicated the normal distribution of the data. In the questionnaire examination, the factor loading of all items was above 0.4, which confirmed the internal correlation. The average value of the extracted variance of the variables was higher than 0.5, and the convergence validity of the measured variables was favorable. Composite reliability was obtained as more significant than the average variance extracted, which was reported due to the convergence validity of desired measurement models. To check the discriminated validity or divergence, the average value of the extracted variance was more significant than the values of the squared covariance and the maximum squared covariance. The internal reliability of the tools was also confirmed (Table 2). According to Table 3, since the mean square of the extracted variance of all variables was higher than the correlation of the constructs with other constructs in the model, the existence of discriminated validity among the research variables was confirmed, and it showed that the measurement tool had a suitable validity. The impact of the challenges of using biometric evidence in identification was obtained using standard coefficient parameters and significant numbers with SmartPLS software (Figures 1 and 2). The impact and t value of the variable of providing organizational financial resources is 0.817 and 12.78, and the provision of expert human resources was respectively 0.658 and 9.24. Also, the human resources training was obtained as 0.508 and 27.6 in using biometric evidence in identification (Table 4). The t values of the three components showed their confirmation. To investigate the impact of three variables of providing organizational financial resources, providing expert human resources, and the challenges of training human resources in using biometric evidence in identification, a one-sample t-test was used, and the results are presented in Table 4. The value of the t statistic for all variables was more significant than the critical value of 1.96, so all the variables of this research were in good condition. The result of calculating Spearman's correlation coefficient (Spearman's rho) to investigate the relationship between biometric evidence and human resources training was 0.482, and the significance level was p<0.001. Also, the Spearman correlation coefficient for examining the relationship between the use of biometric evidence and the allocation of financial resources was 0.353, and the lack of human resources was 0.519 (p<0.001). Therefore, with 95% certainty, there was a significant relationship between using biometric evidence and human resource training, financial resource allocation, and lack of human resources. The results of the Friedman test to rank the averages showed that the opinions of the respondents were as follows; the variables of human resources training with an average rating of 1.35, allocation of human resources with an average rating of 2.46, and lack of human resources with an average rating of 2.19. Considering that the calculated significance level was lower than the intended significance level (p-value=0.05), it can be said that there was a significant difference between the variables in terms of ranking, and the rank of the variable of human resource allocation was higher than other variables.
MAIN COMPARISON to the SIMILAR STUDIES
According to Figure 2, the research population and the challenges of providing financial resources had the most significant impact on the use of biometric evidence. In other words, in the current situation, using biometric evidence and new methods and tools requires financing and investment in this sector, indicating a need for more attention on the part of the authorities. These results are consistent with the research conducted by Andrey in 2020 [19] and Pari and Hamidi [20]. Also, the challenge of providing expert human resources had a more significant impact on the use of biometric evidence than the challenge of training expert human resources. The use of expert forces in the analysis of laboratory samples (such as the use of biometric evidence in identity authentication) leads to the improvement of correct and accurate evaluation results, which is consistent with the research conducted by Shirzad et al. [21], Zohreh Nedayee [22] as well as Ebrahimi and Sadeghinejad [23]. The results of this research indicated that there needs to be more expert human resources. This issue originates from the need for more recruitment of expert personnel in recent years or the lack of scientific improvement of the existing personnel. The third challenge in this research was training the human forces employed in using biometric evidence in identity authentication, which was a lower priority than the two challenges of providing financial resources and allocating expert personnel. Cultivating skilled people, referred to as human resource development, is an inevitable necessity for organizations to survive and progress in today's ever-changing world.
For this reason, training is considered one of the main tasks of human resources management and is always considered an essential factor in formulating development plans or organizational changes. In any case, simply providing training in the form of courses and training programs cannot be an influential factor in improving human resources in the organization. The research by Nasiri in this field also shows the importance of training [24].
LIMITATIONS
The limitations of this research were the non-cooperation of some detectives and police officers of the province, despite sufficient explanations by the researchers. As a result, they did not participate in this research and did not complete the questionnaire.
SUGGESTIONS
It is suggested to increase this department's budget due to the urgent need for identification methods and the increase in security sensitivities. It is also suggested to provide and reduce the costs by exploiting private investment departments.
CONCLUSIONS
Based on the research, the lack of adequate financial resources plays a significant role in using biometric evidence for identification. Also, the variable of providing expert human resources and training employed human resources are in the following steps. The use of biometric evidence and the use of new methods and tools require financing and investment, which indicates a lack of attention on the part of the authorities, and the lack of expert human resources can originate from the lack of recruitment of specialists in recent years or the lack of scientific improvement of the existing forces. Also, providing training in the form of courses and training programs cannot be an influential factor in improving human resources in the organization.
ACKNOWLEDGMENTS
We appreciate all the detectives, intelligence officers, and officials of Gilan province in Iran who cooperated in conducting this research.
CONFLICT of INTEREST
The authors state that the present study has no conflict of interest.
FUNDING SOURCES
The present study did not have any financial support.
Table 1) used tool (questionnaire developed by the researcher)
| Question | very much | much | medium | Low | very low |
| How familiar are you with the new biometric method? |
| How much can new biometric methods help police detectives in crime detection? | |||||
| What is the amount of training provided by the organization to identify new methods? | |||||
| How much does the lack of continuous training in this field affect the performance of intelligence police detectives in crime detection? | |||||
| Do you agree with the feeling of not needing necessary training in biometrics? | |||||
| How much is the lack of interest in learning new biometric methods? | |||||
| What is the amount of training the organization provides to identify new methods? | |||||
| How much progress has been made in training human resources in the application of new biometric methods in the country? | |||||
| Is the lack of proper budget allocation for providing education effective in providing new biometric methods? | |||||
| Does the authorities' lack of knowledge about the applications of the new biometric method in crime detection, as a result of the lack of appropriate budget allocation, affect the use of this method among police detectives? | |||||
| Does not allocating appropriate funds for purchasing and setting up new equipment and facilities harm the detection of organized crimes? | |||||
| Does the authorities' lack of feeling the need to allocate funds for equipping and launching new biometric tools harm the detection of organized crimes? | |||||
| What is the existence of sanctions and their impact on buying and equipping a biometric laboratory? | |||||
| How much is the provision of organizational resources in the use of new biometric methods? | |||||
| How effective is the absence of experts in the field of new biometric methods? | |||||
| How prepared are the human resources of the organization to use the new biometric methods correctly? | |||||
| Is the absence of feeling the need to train specialists in the field of new biometric methods evident? | |||||
| How successful do you think the use of new biometric methods will be in Police Headquarters? | |||||
| How much has been achieved in the provision expert human resources in the use of new biometric methods in the organization? | |||||
| Does the frequent transfer of specialists affect the lack of motivation of these people? | |||||
| Does the lack of proper motivation of people in using new biometric methods affect the learning of these methods? | |||||
| Does not employing experts in the field of new biometric methods cause the failure of using this method? | |||||
| Does the absence of a long-term strategic plan in the field of new biometric methods harm the use of this method? |
Table 2) Reliability table of measurement tools
| Variable | Cronbach's alpha coefficients | composite reliability | Maximum squared common variance | The square of the common variance |
| Challenges of providing organizational financial resources | 0.717 | 0.887 | 0.522 | 0.271 |
| The challenges of providing expert human resources | 0.791 | 0.825 | 0.302 | 0.298 |
| Challenges of training human resources | 0.755 | 0.843 | 0.246 | 0.192 |
| Using biometric evidence | 0.951 | 0.779 | 0.286 | 0.204 |
Table 3) Correlation matrix of variables
| Variable | AVE | Challenges of providing organizational financial resources | The challenges of providing expert human resources |
Challenges of training human resources |
Using biometric evidence | |
| Challenges of providing organizational financial resources | 0.663 | 0.814 | 0.814 | - | - | - |
| The challenges of providing expert human resources |
0.591 | 0.768 | 0.683 | 0.701 | - | - |
| Challenges of training human resources |
0.584 | 0.764 | 0.706 | 0.624 | 0.764 | - |
| Using biometric evidence | 0.535 | 0.731 | 0.683 | 0.565 | 0.668 | 0.796 |
Figure 1) The relation between the challenges of providing organizational financial resources and expert
human resources and their training with the use of biometric evidence and the use of the parameter of standard coefficients.
Figure 2) The relationship between the challenges of providing organizational financial resources, providing expert
human resources and training human resources with the use of biometric evidence using the parameter of significance.
Table 4) One-sample mean t test of research variables
| Variable | Mean | standard deviation | t | Confidence Interval |
Significance level | average interval | |
| lower limit | upper limit | ||||||
| Challenges of providing organizational financial resources | 3.616 | 0.718 | 8.270 | 0.468 | 0.764 | p<0.001 | 2.532-3.764 |
| The challenges of providing expert human resources | 3.272 | 0.507 | 5.175 | 0.167 | 0.377 | p<0.001 | 2.833-3.377 |
| Challenges of training human resources | 3.437 | 0.606 | 6.967 | 0.313 | 0.562 | p<0.001 | 2.687-3.526 |
| Using biometric evidence | 3.505 | 0.593 | 8.216 | 0.383 | 0.627 | p<0.001 | 2.617-3.627 |
The test value was 3.
Article number: e29
Article Type: Original Research |
Subject:
Forensic Medicine
Received: 2022/05/12 | Accepted: 2022/07/27 | Published: 2022/09/11
Received: 2022/05/12 | Accepted: 2022/07/27 | Published: 2022/09/11
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