The Learning Method a Humanistic Perspective

Biometrics is a concept that has attained significant popularity in recent years due to the prevalence and capabilities of technology. Biometrics is a form of access control that can accurately, efficiently, and uniquely identify humans. Biometrics can be identified as either physiological or behavioral characteristics (Saferstein and Roy 153). The concept of forensic biometrics seeks to use automated and human-based systems to identify, analyze, and interpret biometric data for forensic and investigatory activities.

Origin and Development

Considering that biometrics consists of several elements, including iris, fingerprint, and facial recognition, alongside behavioral characteristics – each has a unique history of origin and development. The first attempts of using the iris for an identification came in the 1950s by J.H. Doggart who emphasized that the iris is unique for each individual forming infinite patterns. By 1985, Dr. John Daugman created an algorithmic computer system that could analyze and verify the human iris, which became known as the IrisCode and is the foundation of most modern iris scanners. Similarly, technology has also focused on scanning the retina of the eye, which also has a unique pattern of veins and capillaries but remains less consistent and reliable than iris biometrics (Saferstein and Roy 158).

Similarly, facial recognition is a non-intrusive and efficient mechanism of biometrics. Facial recognition amongst people in society has been used historically for centuries (wanted posters), but modern technology is attributed to Matthew Turk and Alex Pentland in the 1990s. They created the Eigenface technique which uses matrices of human faces to identify and authenticate human faces. It was an automated biometric solution that allowed the computer to effectively track faces from a camera without error or human intervention (Saferstein and Roy 161).

Fingerprints are the earliest known biometric system, with some accounts of its use as identifying marks dating back to 500BC in the Babylonian Empire. In 1896, Sir Edward Henry created the Henry Classification System which sorted fingerprints by physiological characteristics for quick searching and became the first official system of identification used by law enforcement and later became the basis for modern databases (Lee). Currently, law enforcement has huge databases, transitioning from IAFIS to NGI, which stores millions of fingerprints due to the Tenprint techniques in addition to the NPPS with palm prints. These databases can use complex algorithms to match up fingerprints of most citizens in a matter of minutes (Saferstein and Roy 164).

Therefore, the biometric boom occurred in the 1990s when biometrics grew significantly as a field of research. In combination with the onset of the digital age, computerized algorithms and rapid automation could be combined with traditional techniques and databases that law enforcement agencies have been developing for decades. The gradual integration of biometrics into commercial technology has also significantly helped to boost the research and applications of forensic biometrics.

Physical Evidence

Biometric systems use biological traits (modalities) which are known in advance and used for person recognition. Recognition occurs in real-time depending on the computational efficiency of biometric applications (Jain & Ross 4). Forensic biometrics relies on a mixture of physical and digital evidence. Some forensic concepts developed for physical evidence can be applied to digital evidence, but others cannot be due to its nature. Biometric technologies can be utilized to process data from latent fingerprints and palmprints as well as written documents as purely physical evidence. Recovering latent fingerprints, including their age, can be very important, particularly when conventional methods may not work effectively on surfaces such as metal or in unfavorable conditions. Using tools such as high-resolution optical capturing devices or electromagnetic spectrum tools (infrared to X-ray) can track potential biometrics even in a covert mode. These treatments meant for latent fingerprint visualization allow the physical evidence to be digitized and analyzed through numerous databases to search for offenders (Tistarelli et al. 157).

Generally, the physical or biological evidence which may be potentially applicable in forensic biometric matching such as fingerprints, sources of DNA, or physical audio or visual recordings are collected, treated, and stored as they would with traditional forensic techniques. It is a well-developed process for effective identification, documentation, collection, and preservation which seeks to follow scientific analysis and maintain the integrity of the evidence. In a forensic context, a sample obtained from the crime scene is referenced to existing samples and needs to be of sufficiently good quality for the biometric system to perform as intended. However, biometric traits on evidence need to be unique, distinctive, and robust to the forensic conditions. Therefore, the quality of the sample is reliant on the integrity of evidence collection and environmental conditions at the scene (Saini and Kapoor, 3)


Although more common in the military rather than law enforcement, one preliminary test for biometrics used is Sensitive Site Exploration (SSE). It can be defined as systematically searching for and collecting information and material from a designated location and analyzing them to answer information requires, facilitating subsequent operations, or supporting criminal persecution (Blestrieri). Essentially, it is the use of special exploitation kits which can capture biometric data at the scene (retina, facial, DNA, and fingerprints) as well as analyze personal documents and communications, and provide a limited but efficient overview of available intelligence. The confirmatory test, such as for fingerprints, can be considered AFIS searching. It is conducted in a secure and stable location after the evidence was collected and processed. The fingerprint is submitted into the automated fingerprint identification system (AFIS) at which point algorithms automatically return a number of most likely potentially matches, at which point the human examiner has to confirm the match to avoid potential error (Kellman et al. 2).

Identification Processes and Applications

Biometrics at its core is a verification mechanism that is meant to identify an individual based on their physiological or behavioral traits. The biometrics expansions can be observed in various forensic identification parameters such as the face, fingerprint, iris, voice, handwriting, and others. The biometrics system can be classified into two categories, identification, and verification. In the identification mode, the biometric system attempts to identify the individual by searching the templates stored in the database, conducting one too many comparisons in order to find an identity. In verification mode, the biometrics of an individual is compared to the biometric template stored in the system database, known as a one-to-one comparison (Saini and Kapoor, 2).

As discussed earlier, each type of biometric is inherently unique to an individual, ranging from fingerprint patterns to iris capillaries to distinctions in voice and handwriting. Modern technology uses complex sensors and analysis to identify and track these individual traits in biometric characteristics and compare them to databases in order to present the closest matches. Since the system is yet imperfect, usually algorithms err on the side of caution and require subsequent confirmation of matches by human beings who evaluate the parameters and final results of the algorithms.

Forensic biometrics play a role in crime detection in a combination of applications. The techniques and modules of biometrics analyze evidence by overcoming human sensory, cognitive, or simply time/physical limitations; thus, increasing both the efficiency and the effectiveness of investigations using forensic biometrics. Second, the methods applied to provide a scientific and data-oriented basis to criminal investigation procedures and investigation, also going beyond human capabilities by applying computer science, mathematics, and statistics to large data. Finally, the methods help to standardize the elements of evidence analysis and criminal identification, decreasing human biases or errors (Saini and Kapoor, 2).

Case Studies

One example of forensic biometrics being utilized was in the aftermath of the Boston Marathon bombing of 2013. At the behest of the FBI, tips were pouring in with photographs and videos. Each was meticulously analyzed, and face identifications were compared to databases of terrorists or persons of interest. However, the system at the time failed to recognize the terrorists and provide identification, the Tsarnaev brothers, even though government agencies had their up-to-date photographs on hand and one was involved in a terrorism-related investigation, all of which was digitally recorded. However, the biometric system at the time failed to recognize them clear in several of the photographs due to the low resolution of photos and one of the terrorists wearing sunglasses. It is a challenge that modern systems to this day struggle with, but with Next Generation Information System implementation, there is potential for improvement (Saferstein and Roy). As technology improves, both visual capturing (including on commercial devices) and digital analytics algorithms meant to search and compare databases, the biometric system can potentially experience greater success.

One noted benefit of the new technology and biometrics databases is that it allows law enforcement to revisit cold cases. Recently, a 30-year-old murder case was resolved using the FBI’s Integrated Automated Fingerprint Identification System (IAFIS). A 61-year old senior was brutally stabbed in 1978 in his apartment. After which the perpetrator stole the victim’s car and escaped. Police were able to collect evidence, including latent fingerprints and palmprints in the apartment, and later, in the found care, but no new leads could be found despite fingerprints being manually compared to local and state files. In 2008, after receiving an inquiry on the case, an office at the Omaha Police Department ran the latent fingerprints through the IAFIS. After investigating possible matches, she came up with positive identification of a known felon currently serving prison time. An investigation was opened, placing the perpetrator in the vicinity of the crime, and subsequent DNA testing proved to be a match, allowing for the prosecution of the dangerous criminal placing him in prison for life (“30-Year-Old-Murder Solved”). Repeatedly, the FBI notes that cold or contested cases can significantly benefit from modern biometric systems as technology allows for a much wider and efficient search of databases than ever before.


In modern society, the ability to identify individuals in real-time, both reliably and effectively, is the foundation of various applications of biometrics in a highly networked world. Forensic biometrics seeks to take advantage of this, both in real-time and post-event collection of evidence at a crime scene. The biometric modalities are algorithmically analyzed in complex databases which allow for rapid identification of individuals and offenders. As technology and networks continue to improve and become prominent in society, biometric data will become central to various applications, including forensic investigations.


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McLeod, S. (2017). Kolb’s learning styles and experiential learning cycle. Simply Psychology. Web.

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ChalkyPapers. "The Learning Method a Humanistic Perspective." October 10, 2023.