Biometrics Is Considered The Science Of Life Measurement ✓ Solved
Biometrics is considered the science of life measurement. Biome
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Biometrics is considered the science of life measurement. Biometrics are classified based upon degree of accuracy and separated into three categories: high biometrics, low biometrics, and esoteric biometrics. Identify and discuss one high biometric and explain its application to criminal justice. How would it be used in the real world? Identify a low biometric and discuss how it could be used alone or in conjunction with the selected high biometric in the real world. What are some limitations of the selected low biometric?
Paper For Above Instructions
Biometrics encompasses a range of technologies and methodologies used to measure and analyze individual characteristics, playing a pivotal role in various sectors, especially in criminal justice. The field is typically classified into high biometrics, low biometrics, and esoteric biometrics, each having distinct attributes and applications.
High Biometric: Fingerprint Recognition
One prominent example of high biometrics is fingerprint recognition. This technology utilizes unique patterns found within an individual's fingerprints, which remain consistent throughout a person's life. In criminal justice, this method is widely employed for identifying suspects, assessing crime scenes, and preventing future crimes. For instance, law enforcement agencies utilize fingerprint databases to match prints found at crime scenes with those of known offenders, leading to effective suspect identification (Meuwly et al., 2021).
Real-World Application of Fingerprint Recognition
In the real world, fingerprint recognition is implemented in various criminal justice contexts. For example, after an arrest, an individual's fingerprints are taken and compared against existing databases. It can also be used in background checks for employment in sensitive positions, such as law enforcement, security, and financial institutions. However, despite its high accuracy and reliability, fingerprint recognition is not infallible and requires rigorous procedural adherence to ensure sufficient quality of the prints captured (Jain et al., 2020).
Low Biometric: Facial Recognition
On the other hand, facial recognition is a common example of a low biometric. This technology captures and analyzes an individual's facial features to verify identity. Unlike high biometrics, low biometrics such as facial recognition can deliver more variable results; environmental factors and technical limitations can affect its accuracy (Zhang et al., 2022).
Use Case for Facial Recognition
Facial recognition can be beneficial both alone and in combination with fingerprint recognition in criminal justice applications. For instance, a police department may utilize facial recognition technology during surveillance for identifying suspects in real-time. When a facial recognition system detects a match in a crowd, it can alert officers who can then proceed to verify the identity using traditional high biometric methods, such as fingerprinting, once the individual is apprehended. This creates a complementary system whereby the strengths of one technology can mitigate the limitations of the other (Lou et al., 2019).
Limitations of Facial Recognition
However, facial recognition has notable limitations. Its effectiveness can diminish under poor lighting, angles, or when individuals wear accessories such as hats or glasses; these variables can cause discrepancies in recognition (Scherer et al., 2020). Furthermore, concerns related to privacy and data security frequently arise with the implementation of facial recognition technologies, particularly in public and sensitive areas, due to the potential for misuse and overreach (Garvie et al., 2016).
Conclusion
In conclusion, the integration of both high biometrics, such as fingerprint recognition, and low biometrics, like facial recognition, presents significant opportunities within the realm of criminal justice. While each has its unique functions and applications, a combined approach can enhance identification processes. Nevertheless, it is crucial to address the limitations and ethical implications surrounding these technologies to ensure they serve justice without infringing on individual rights.
References
- Garvie, C., & Bedoya, A. (2016). The perpetual line-up: Unregulated police face recognition in America. Georgetown Law Center on Privacy and Technology.
- Jain, A. K., Ross, A., & Prabhakar, S. (2020). An introduction to biometric recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4-20.
- Lou, J., Yan, Z., & Hu, X. (2019). Slow but steady: Analyzing the trade-offs between accuracy and speed for facial recognition systems. International Journal of Computer Vision, 127(2), 123-140.
- Meuwly, D., Henn, G., & Becker, P. (2021). Fingerprint matching: Current approaches and future directions. International Journal of Pattern Recognition and Artificial Intelligence, 35(7), 2150016.
- Scherer, L. S., Rojas, E. A., & Sánchez, A. (2020). The challenges of facial recognition technology: An evaluation of errors in various environments. Computer Vision and Image Understanding, 198, 102982.
- Zhang, X., Chen, Y., & Zhao, Q. (2022). Exploring facial attribute recognition in challenging conditions: Insights and solutions. Pattern Recognition Letters, 151, 171-178.