Definition of Terms
Next Generation Identification (NGI) is a major innovation in criminal justice that will facilitate in increased evidence collection. NGI is a Federal Bureau of Investigation’s (FBI’s) project aimed at broadening the capacities for the Integrated Automated Fingerprint Identification System (IAFIS) by law enforcement to recognize suspects by looking at their history of crime and their fingerprints. The IAFAS is a computerized system by the FBI, which collects criminal history and identifies fingerprints automatically (Stokes, 2019) . It offers latent searching abilities, electronic storage of images, searching fingerprints automatically, and exchanging responses and fingerprints electronically.
History
Starting July 1999, the CJIS (Criminal Justice Information Services) Division maintained and operated the biggest person-centric database in the world. Due to increasing threats, new capabilities for identification became essential (Shekhar & Murthy, 2016) . Technological advancements created room for additional development of biometric identification approaches. The CJIS Division, under the guidance of the user community, created the NGI system aimed at meeting the evolving crime identification needs (Sankaran, Jain, Vashisth, Vatsa, & Singh, 2017) . The FBI improved the IAFIS’s, introducing the NGI biometric identification system and advance approaches of collecting criminal history information.
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Current Strategies
Presently, biometrics does not just revolve around fingerprints. It also comprises of irises, palm prints, as well as facial recognition (Bang, Park, Yoon, Sin, & Lee, 2019) . In an aim to incorporate new technologies and boost the application of latent and tenprint, the FBI ’s NGI offers the criminal justice community with the most efficient and biggest electronic repository of criminal history and biometric information (Vatsa & Nixon, 2019) . Biometrics has grown increasingly vital for the FBI, the intelligence community, and the law enforcement with the continued search for technological advancements to improve the quality and range of its investigative capabilities and identification.
Current/Impending Advancements
The present advancements in the NGI innovation revolve around incorporating the system for both the law enforcement as well as the non-law enforcement goals. The FBI plans introducing the technology to the national, federal, state, local, and tribal levels (Araújo, Chaves, & Lorena, 2019) . The technology is also being introduced to private organizations not affiliated to law enforcement agency (Short, Yuffa, Videen, & Hu, 2016) . By utilizing crowds’ facial recognition images, NGI will facilitate the identification of people in public settings making it possible to identify criminals in all areas.
Recommended Advancements
The recommendations to the NGI technology will entail ensuring to have databases of all individuals to make it possible to identify people once a crime occurs. The system should collect images of people as well as other biometric identifiers (Shekhar & Murthy, Affine-scale invariant feature transform and two-dimensional principal component analysis: A novel framework for affine and scale invariant face recognition, 2016) . Furthermore, it will be essential to collect the photos of drivers’ licenses and other biometric records, which will be incorporated into the system. The NGI system should also be integrated to other surveillance technology, including Trapwire, which would facilitate in matching of real time images from CCTV surveillance cameras (Walton, 2017) . It will be crucial to integrate the NGI system with the CCTV cameras that both private and public agencies operate to improve accuracy in gathering information concerning crime.
Justification for Recommendation
The need for broadening the reach of the NGI system revolves around the idea that crimes are becoming increasingly complex. Technology is also advancing at a rapid pace making it possible to capture a wide range of criminals (DiBIase & Walton, 2017) . Thus, implanting the NGI will provide an avenue for collecting increased evidence related to crime, which will improve efficiency in dealing with various kinds of crimes.
Plausibility and Logistics of Implementation
To ensure that the NGI system works efficiently in dealing with the various kinds of crimes in the current society, a need will arise for both private and public agencies to work together in information gathering (Stokes, 2019) . The entities can share the information gathered to ensure it becomes more accurate to facilitate in crime evidence identification.
Anticipated Impact of Implementation of recommendations
With the implementation of the suggested recommendations, it will be probable to process increased volumes of criminal data with high accuracy and faster. Increased speed of processing criminal data and with high accuracy will ensure that law enforcement agencies manage to boost the effectiveness of safeguarding the public (DiBIase & Walton, 2017) . The reason for this is that they will boost the efficiency of data gathering, which will make it possible to identify individuals based on their likelihood for committing various kinds of crimes.
References
Araújo, E. J., Chaves, A. A., & Lorena, L. A. (2019). Improving the clustering search heuristic: An application to cartographic labeling. Applied Soft Computing, 77 (1), 261-273.
Bang, J. S., Park, W. J., Yoon, S. Y., Sin, J. H., & Lee, Y. T. (2019). Trends of intelligent public safety service technologies. Electronics and Telecommunications Trends, 34 (1), 111-122.
DiBIase, T., & Walton, R. H. (2017). Long-term missing-persons and no-body cold case investigations. Lanham: CRC Press.
Sankaran, A., Jain, A., Vashisth, T., Vatsa, M., & Singh, R. (2017). Adaptive latent fingerprint segmentation using feature selection and random decision forest classification. Information Fusion, 34 (1), 1-15.
Shekhar, V. S., & Murthy, K. B. (2016). Affine-scale invariant feature transform and two-dimensional principal component analysis: a novel framework for affine and scale invariant face recognition. Let Computer Vision, 10 (1), 43-59.
Shekhar, V. S., & Murthy, K. B. (2016). Affine-scale invariant feature transform and two-dimensional principal component analysis: A novel framework for affine and scale invariant face recognition. Let Computer Vision, 10 (1), 43-59.
Short, N. J., Yuffa, A. J., Videen, G., & Hu, S. (2016). Effects of surface materials on polarimetric-thermal measurements: Applications to face recognition. Applied Optics, 55 (19), 5226-5233.
Stokes, J. (2019). Next generation identification—A powerful tool in cold case investigations. Forensic Science International, 299 (1), 74-79.
Vatsa, M., & Nixon, M. S. (2019). Editorial Introducing the IEEE Transactions on Biometrics, Behavior, and Identity Science. IEEE Transactions on Biometrics, Behavior, and Identity Science, 1 (1), 1-2.
Walton, R. H. (2017). FBI criminal justice information services and the National Crime Information Center. Lanham: CRC Press.
