Fingerprint Evidence

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Francis Galton, Henry Faulds and William Herschel often get the credit when it comes to fingerprint identification systems. The first criminal conviction was accomplished in 1892 thanks to Juan Vucetich’s fingerprint system being used 10 years prior to this evidence being used in cases in England and Paris. (Teitelbaum, 2018). A woman by the name of Rojas accused a man by the name of Pedro of smashing her children’s heads in because she refused to marry him. Not only did she accuse him of that, but she also accused him of nonfatality slicing her neck. After intense questioning to Pedro, Officer Alvarez went to look further into the case. Vucetich, inspired by Galton’s work, decided to extend the fingerprint to all 10 fingers and invent a unique system of classifying fingerprint patterns.

Alvarez, who was fascinated by Vucetich’s work, understood its potential within criminal investigations. He shortly discovered than Rojas’s current lover didn’t want the kids to be a part of the picture, so Alvarez went to the hut to find to collect more evidence and that’s when he found a print. He sawed off a piece of the door where the print was on and acquired prints from Rojas. With a magnifying glass and the classifying system Vucetich invented, it was discovered the prints of Rojas and the suspect print was identical and she confessed. The success of this system not only resolved several proceeding cases but also saw the abandonment of the Bertillon measurement system in Argentina. It also saw that this system would be used mostly in South America, China, Japan, and many other non-European countries.

Fingerprinting evidence is often seen as valuable evidence because it is made up of curve segments such as the minutiae and valleys that always distinguishes one person form another (Mohan, Anand, Varghese, Aravinth, & Dolly, 2019). Within the minutiae of a fingerprint is the ridge endings, which is the point where the ridge ends abruptly, and ridge bifurcations, which are where the ridge cuts into two. These features are studied by examiners only after extraction measures are taken to receive the minutiae points, ridge features that are observed at the ridge ending and/or bifurcation. The measures used to extract these points are image acquisition, fingerprint enhancement methods, thinning plus digitization, edge detection and lastly minutiae extraction in the sequential order stated. (Mohan, 2019).

The objective of image acquisition is to transform the fingerprint image into a grey scale image to clarity purposes. The result is that the dark lines represent the ridges and the valleys are lighter than the former. The next step is using fingerprint enhancement techniques. Using the histogram modification or contrast enhancement enhances the “contrast of an image by regulating the intensity of every gray level of the image.” (Mohan, 2019, paragraph 7). The result is that lower quality areas could receive a higher contrast which is advantageous to the examiner.

The next step is thinning and digitization which is digitizing the ridges of the image into ones and the valleys into zeros. Thinning is then operational by reducing the binary image so the ability to find ridge endings and bifurcations will be greatly easier. Edge thinning comes into the fray by weeding out the less important information form the fingerprint image have only the important information left in. It’s performed by getting points on the image where the grey scale varies significantly. The last method involves minutiae extraction and matching. Observing the enhanced image with its bifurcations and ridge endings coded against the reference image is done now by lining them up in the same direction and determining if they are a match or if they is a need for furthering investigation.

When fingerprint evidence is thought of, one may ask if certain characteristics of the fingerprint will affect the quality of the fingerprint. Ryan Hancock and Stephen Elliot studied and recorded an experiment to test out this theory. Their initial hypothesis was that those finger characteristics included in the research such as skin texture, pigmentation, color, elasticity, keratin level and finger minutiae had no relationship with quality of the fingerprint. (Hancock and Elliot, 2016). Hancock and Elliot’s (2016) study involved collection of 8,000 fingerprints and ran them through a fingerprint sensor.

The Moritex Pro Device collected the characteristics of elasticity, skin pigmentation, keratin level, skin texture and color. The skin temperature and the finger minutiae were not included within the Moritex Pro Device, so they were taken with a standard thermometer and biometric extractor respectively. The experiment ran through a best-subsets test using the variables and displayed in chart form. The result was that there wasn’t a relationship shown between the fingerprint characteristics and the image quality of the fingerprint.

The following study focused on fingerprint patterns and the relationship between that and blood groups and gender. This study was designed to be able to pair fingerprint patterns to blood groups and gender which can credibility of fingerprinting in catching potential criminals. The study was performed in Hubli- Dharwad of Karnataka, India, and 200 subjects had their finger recorded with 100 males and 100 females participating. The blood groups of everyone were also noted within the study. The blood was categorized into the A, B, O, AB, Rh+ and Rh- groups. The fingerprint patterns were categorized as either whorl, arch, or loop. (Bhavana, J.L, Ruchi, & Prakash, 2013).

After the recording of all the variables’ information were collected numerous results were found. Whorls were more frequent within the blood group of O negative. Arches and Loops were in the highest frequency within the blood groups of B and O. Blood group AB, in comparison to other groups, had the least number of arch patterns. Blood group A has more Loops than either arches or whorls. Loops and Arches were frequent among women and men were more associated with whorls. (Bhavana et al, 2013).

Ulery, Hicklin, Buscaglia & Roberts (2011) study was based on the idea that fingerprint evidence is only as reliable as how much experience the latent fingerprint examiner has. This study was brought about to test the accuracy and reliability of their decisions based on the evidence. The number of false positives and false negatives were the variables being studied based on the evidence the examiner’s got to examine. There were 169 examiners with high levels of experience and only 83% were certified. (Ulery et al, 2011). These examiners would review 744 latent print pairs, and many would receive the same pairs separately to understand the variability among each subject’s decisions.

Within the study, these prints would vary in attributes and in quality (low, medium and high) to also understand the variability and the consensus among examiners. According to the results of the study, “False positive errors were made at the rate of 0.1% and never by two examiners on the same comparison. Five of the six errors occurred on image pairs where a large majority of examiners made true negatives.” (Ulery, 2011, p. 7738). Overall, 5 out of the 169 examiners performed these errors. Additionally, false negative errors occurred at a rate higher at 7.5. A majority of these examiners performed a minimum of one false negative. The study indicates that blind verification, another examination by an examiner with skills on par or better than the original examiner, could have decreased the number of false positives and negatives.

Fingerprint evidence is a fundamentally aspect of the criminal justice system and investigations, but it may not always lead to a conviction. Fingerprint evidence works best with others forms of evidence which is exemplified in the Raekwon DaRel Collins v. Commonwealth 2017 case. The case follows a woman named Cheryl Blands having her house broken into having three flat-screen televisions stolen from her home. A neighbor called the police after seeing some people running away with sacks in their hands. The police shortly arrived to find the neighbor who assisted the officers in finding the televisions in the sack near the backyard of her house. These were the belongings of Cheryl Blands. Before returning the televisions to Bland when she returned the investigator accompanying the officers lifted several prints from the television.

At the police department, a fingerprint examiner rolled Raekwon’s prints to compare them with the suspect prints. The examiner found they were a match but only with his left index and middle finger on one of the televisions. The other prints couldn’t be matched with Raekwon’s prints. Since he had no alibis or testimony, he was charged with grand larceny and burglary. He appealed and got the conviction overturned due to lack of sufficient evidence.

In Virginia, the rule for fingerprint evidence is that it has to be in combination with other evidence to exclude any possibilities or doubt that the fingerprint was there at any time except for the crime. (Lightle, 2018). The department only had two prints of the appellant and none including the thumb which meant that there was a possibility he didn’t even pick the television up. Because of that window of doubt and time between what the neighbor saw and when the officers found the television, the conclusion was that the court was wrong for indicting Collins on fingerprint evidence only and the case was reversed.


  1. Bhavana, D., Ruchi, J., Prakash, T., & J.L., K. (2013). Study of Fingerprint Patterns in Relationship with Blood group and Gender- a Statistical Review. Research Journal of Forensic Sciences, 1(1), 15–17.
  2. Hancock, R., & Elliott, S. (2016). Evidence of correlation between fingerprint quality and skin attributes. 2016 IEEE International Carnahan Conference on Security Technology (ICCST). doi: 10.1109/ccst.2016.7815708
  3. Lightle, R. (2018). Fingerprints alone insufficient evidence for conviction. Virginia Lawyers Weekly.
  4. [bookmark: _Hlk34579545]Mohan, P., Anand, S., Varghese, R. B., Aravinth, P., & Dolly, D. R. J. (2019). Analysis on Fingerprint Extraction Using Edge detection and Minutiae Extraction. 2019 2nd International Conference on Signal Processing and Communication (ICSPC). doi: 10.1109/icspc46172.2019.8976803
  5. Teitelbaum, J. (2018). The First Criminal Conviction Based on Fingerprint Evidence: Argentina, 1892. Forensic Science Review, 30(1), 16–17.
  6. Ulery, B. T., Hicklin, R. A., Buscaglia, J., & Roberts, M. A. (2011). Accuracy and reliability of forensic latent fingerprint decisions. Proceedings of the National Academy of Sciences, 108(19), 7733–7738. doi: 10.1073/pnas.1018707108

Cite this paper

Fingerprint Evidence. (2022, Jan 14). Retrieved from https://samploon.com/fingerprint-evidence/

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