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Introduction
Any workplace is not insured against the crime. Theft of the workspace is a severe crime that needs to be taken into account with all the associated countermeasures. In the case of Acme Logistics, the equipment is stolen during business hours through the facility doors without any identification technologies. While the most effective solution to the problem may be the installation of hidden cameras, the notice requirement is necessary for video surveillance due to the invasion of privacy concerns (Findlaw, 2018). Therefore, Acme Logistics has decided to catch the culprit by utilizing biometrics and surveillance systems with advanced tracking. This approach would allow to mitigate future crimes and identify the culprits without initiating additional lawsuits regarding privacy concerns. The current business proposal analyzes the theft scenarios and suggests several recommendations on how to prevent consequent crimes and identify the perpetrators.
Situational Breakdown
In the current situation, the primary objective is not only to stop equipment thefts but also to catch the culprit. For this purpose, it is critical to improve security measures and implement recognition systems to identify criminals. All employees have been asked about the crimes already; therefore, it is safe to assume that the culprit would be additionally cautious from this point on. Furthermore, there are only three public doors with no authorization control, so it is possible to implement additional security measures specifically on these points of entrance. It would further complicate the theft process for the culprits due to additional emphasis on the crime scenes. Nevertheless, if the crimes continue, it is essential to install biometric systems to identify the perpetrators and stop the thefts. Consequently, the choice of the security framework should depend on the value of the stolen equipment and implementation costs.
Possible Solutions
After acknowledging the scope of the problem, it is necessary to implement the required technologies in the workspace. Among the security systems, gait biometrics and video surveillance have proved to be effective not only in preventing crime but also in identifying the culprits by utilizing complex methods. For instance, gait recognition might be utilized in combination with weight calculation to detect any stolen equipment. Furthermore, biometric systems are continually becoming more technologically advanced and easier to implement, making them the appropriate choice for security purposes.
Selective Gait Tracking
One of the possible solutions to the problem is the implementation of smart visual surveillance with gait tracking. Such systems will only record the data after the detection of employees in the range of the video surveillance (Bouchrika, 2018). The selective recording allows to save space in the system and increases the quality of visual data, which, in turn, would assist in identifying the culprits (Bouchrika, 2018). Furthermore, gait recognition is a specifically effective system for such purposes since it does not require cooperation from the subjects (Chao et al., 2019). The factors that may obstruct the performance of gait biometrics are the distortion of a silhouette, walking speed, and the technological capabilities of the cameras (Chao et al., 2019). Arguably, the implementation of silhouette-based methods might deteriorate the accuracy of identification due to the emphasis on the overall appearance of the person instead of facial features (Bouchrika, 2018). Nevertheless, gait recognition has been proved to be a highly effective method of biometric security regardless of the selected approach and sub-category of the framework.
Consequently, gait biometrics has a number of transparent advantages that might be efficient in identifying the culprit. A system based on gait tracking cannot be deceived by make-ups, masks, or hats that the criminals might use to alter their visual appearance (Bouchrika, 2018). In such cases, gait recognition is superior to facial biometrics since the crimes in Acme Logistics are recurrent, and the criminals might be aware of innovative protection systems. As a result, after learning about the investigation, the culprits might alternate their visual appearances to deceive most of the biometric systems. However, gait recognition is a behavioral framework that is difficult to deceive intentionally.
Furthermore, some of the most notable advantages of gait recognition include the possibility to identify culprits from a considerable distance and the non-invasive nature of recognition. The latter implies the possibility of recording the data without cooperation or awareness from the subject, which is an effective approach to identify culprits and avoid legal concerns associated with cameras (Sabhanayagam et al., 2018). Lastly, gait recognition can be utilized to improve the overall quality of video surveillance in the workspace (Bouchrika, 2018). Gait biometrics is a unique system that might be accurate even with the lowest video quality, unlike facial recognition, or it might be used in combination with motion detectors to implement selective recording (Bouchrika, 2018). Both models are effective in increasing the overall quality of video surveillance security and identifying the culprits.
Gait Tracking Costs and Technological Capabilities
Additionally, gait biometrics are relatively cheap and easy to implement in public areas. At present, there are three primary types of gait biometrics video-based, floor-based, and wearable sensors (Yalavarthi et al., 2019). Among these systems, wearable sensors are associated with the lowest costs and the least number of potential drawbacks; however, it is not an appropriate choice for identifying criminals in the workspace (Yalavarthi et al., 2019). Concerning the costs, gait tracking is generally cheaper than its competitors, both in physical and behavioral classifications, if utilized on the basis of standard video surveillance (Alsaadi, 2021). Gait tracking is also associated with the fast processing of the retrieved data (Sabhanayagam et al., 2018). While the speed depends on the selected approach, segmentation and motion detection are unique characteristics that can be extracted without the cooperation of the subjects and analyzed simultaneously (Sabhanayagam et al., 2018). Therefore, gait recognition is an efficient system of both granting security access to employees and identifying the potential culprits.
Dimensions Calculation
Consequently, it is possible to develop a unique biometric recognition system depending on the weight and dimensions of the stolen equipment. Gait biometrics are frequently associated with additional devices that calculate the weight of the person who is granted access. In such cases, the implemented gait biometrics, besides the silhouette and dimensions capacities, might take the weight measurements of the person before and after their visit to the room. For instance, if the weight of the analyzed individual is 70kg before the entrance and 85kg after leaving the room, the person took relatively heavy equipment and should be accountable for that. Therefore, depending on the stolen devices, it is possible to implement specific biometric systems to track the crimes.
Another significant advantage of implementing dimensions calculation in combination with gait tracking is minimizing the drawbacks of biometrics. Gait recognition is notoriously known for inaccurate results in case the analyzed subject is carrying some large objects that obstruct the silhouette of the person (Min et al., 2019). From these considerations, dimensions calculation acts as an additional safety measure to increase the accuracy of the biometrics (Min et al., 2019). As a result, a combination of gait recognition, selective video surveillance, and dimensions calculation is an appropriate choice for securing access to the three public doors and identifying the culprits.
Traditional Security System Implementation
It is also possible to utilize a different approach to prevent crimes in the workspace. At present, biometrics are still associated with high costs compared to more traditional methods of security (Sudar et al., 2019). Token-based techniques, such as passports and smart cards, and knowledge-based techniques, such as passwords and PINs, are still reliable models of data protection (Sudar et al., 2019). Depending on the scope of the implementation and the value of the stolen equipment, the traditional methods might suffice to mitigate crime in the workspace (Sudar et al., 2019). In other words, if the implementation of biometric systems is more expensive than the value of the stolen equipment, it is rational to resort to traditional methods first. Both token-based and knowledge-based techniques are cheaper, less complex, have a physical medium, and might be easily replaced or removed (Sudar et al., 2019). Nevertheless, despite the simplicity, they increase the overall security and might be used to identify the culprit if used in combination with a basic report system.
Ethical Assurances and Legal Concerns
As mentioned before, ethical assurances and legal concerns primarily emerge due to the invasion of privacy. Depending on the state, the laws regarding hidden cameras and mandatory biometric security might differ significantly. In most cases, video surveillance locations should be transparently explained to the employees to avoid any potential legal concerns (Findlaw, 2018). Most violations regarding employee privacy rights and unauthorized usage of video surveillance might become the reason for discrimination and harassment lawsuits (Findlaw, 2018). Nevertheless, in the case of gait recognition, the legal concerns are not as significant due to the non-intrusive nature of biometrics. The employees should still be notified about the presence of video surveillance; nevertheless, there is no legal necessity to explain the principles of gait recognition (Alsaadi, 2021). Ultimately, depending on the state and corresponding laws, the employer should notify the employees about the video surveillance systems and, specifically, hidden cameras.
Conclusion
Summing up, the best possible solution to the theft problem is gait biometrics in combination with selective video surveillance and weight calculation devices. The complex approach to crime prevention by utilizing several security measures would ensure the success of the operation. This method will solve the two primary problems mitigate future crimes and identify the culprits. Furthermore, gait recognition has seen increasing popularity in commercial security, specifically due to the simplicity of implementation and non-intrusive nature of tracking. Therefore, the legal concerns about identification and privacy invasion are less significant for gait recognition than for other physical and behavioral biometrics. Lastly, depending on the value of the stolen equipment, it might be rational to implement traditional methods of security, such as token-based and knowledge-based models, first. These frameworks are associated with lower costs and could be easily replaced or removed after the crimes are stopped.
References
Alsaadi, I. M. (2021). Study on most popular behavioral biometrics, advantages, disadvantages and recent applications: A review. International Journal of Scientific & Technology Research, 10(1).
Bouchrika, I. (2018). A survey of using biometrics for smart visual surveillance: Gait recognition. In P. Karampelas & T. Bourlai (eds.), surveillance in action: Advanced sciences and technologies for security applications (pp. 3-23). Springer International Publishing AG 2018.
Chao, H., He, Y., Zhang, J., & Feng, J. (2019). GaitSet: Regarding gait as a set for cross-view gait recognition. In the thirty-third AAAI conference on artificial intelligence (AAAI-19) (pp. 8126-8133).
Findlaw. (2018). Are hidden cameras at work legal?
Min, P. P., Sayeed, S., & Ong, T. S. (2019). Gait recognition using deep convolutional features. In 2019 7th international conference on information and communication technology (ICoICT). IEEE.
Sabhanayagam, T., Venkatesan, V. P., & Senthamaraikannan, K. (2018). A comprehensive survey on various biometric systems. International Journal of Applied Engineering Research, 13(5), 2276-2297.
Sudar, K. M., Deepalakshmi, P., Ponmozhi, K., & Nagaraj, P. (2019). Analysis of security threats and countermeasures for various biometric techniques. In 2019 IEEE international conference on clean energy and energy efficient electronics circuit for sustainable development (INCCES). Web.
Yalavarthi, V. K., Grabocka, J., Mandalapu, H., & Schmidt-Thieme, L. (2019). Gait verification using deep learning with a pairwise loss. In 2019 international conference of the biometrics special interest group (BIOSIG) (pp. 1-7). IEEE.
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