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Chapter
Colors in Forensics: The Analysis
and Visualization of Forensic Data
and Evidence
Tommy Bergmann, Ronny Bodach, Laura Pistorius,
Svenja Preuß, Paul Seidel and Dirk Labudde
Abstract
In the realm of forensic science, the analysis and visualization of data and traces
rely heavily on the interpretation of colors. This chapter delves into the multifaceted
role colors play in forensic investigations, exploring their significance in various
domains, such as bloodstain analysis, fingerprint examination, image forensics, and
the study of artificial light sources. From discerning the age of bloodstains to identi-
fying manipulated digital images, color analysis emerges as a pivotal tool in unraveling
crime events and establishing facts crucial for legal proceedings. Through a discussion
of selected forensic methods, this chapter highlights the diverse applications of color
analysis and emphasizes the need for standardized approaches to ensure the accuracy
and reliability of forensic investigations. Looking ahead, the continuous advancement
of technologies and methodologies in color analysis promises to enhance further the
efficacy of forensic science in solving crimes.
Keywords: forensic science, color analysis, digital image forensics, bloodstain age
estimation, artificial light sources
1. Introduction
Forensics represents an intriguing discipline encompassing methodologies aimed at
reconstructing crime events to facilitate their resolution [1]. A pivotal aspect of these
methodologies involves the scrutiny of color properties or alterations in evidence.
Light and the associated perception of color have always been integral components
of forensics [2]. One of the oldest tools used at crime scenes was oblique lighting,
which still plays a major role in trace evidence analysis today [3]. The range of color
analysis in forensics now spans the entire electromagnetic spectrum of light [2]. Well-
known examples include making latent biological trace materials visible using UV
light, evaluating or identifying vein patterns in the near-infrared range, and analyzing
pigments in car paints.
Within this book chapter, we embark on a comprehensive exploration of various
domains within forensic science, shedding light on the indispensable role colors
play therein.
1
We commence by scrutinizing blood traces, often characterized by their distinct
blood color associated with their chemical composition. Such analysis can furnish
valuable insights into the timing of the crime, thus aiding in the reconstruction of
events (Section 2.1). Another significant area is dactyloscopy, where fingerprints can
be subjected to electrochemical excitation to glean additional insights into their ori-
gins. Color analysis assumes a crucial role in fingerprint identification and classifica-
tion (Section 2.2). Image forensics emerges as a contemporary field dealing with the
scrutiny of digital images. Here, accurate interpretation of color spaces is imperative
to align the appearance of captured objects with reality as faithfully as possible. This
analysis is pivotal in detecting manipulations or forgeries and verifying image
authenticity (Section 2.3). Lastly, we delve into artificial light sources, which are
pivotal in forensic analysis. Signals captured from such sources can furnish insights
into the timing of the crime and the authenticity of recordings. Color analysis of these
light sources is instrumental in drawing reliable conclusions from the recorded data
(Section 2.4).
The generation, capture, and interpretation of color impressions, employing both
established and novel methodologies, play a pivotal role in forensics. Within this
chapter, we aim to spotlight selected potential applications of color analysis and
underscore its significance in forensic analysis.
2. The term of color in selected forensic methods
Forensics encompasses the scientific discipline dedicated to investigating evidence
and traces in criminal or legal contexts to ascertain facts, establish guilt or innocence,
and aid in the resolution of crimes. It encompasses various subfields, including
criminalistics, forensic medicine, digital forensics, forensic psychology, and forensic
pathology. These disciplines entail the examination of physical evidence at crime
scenes, the analysis of DNA and other biological materials, scrutiny of digital evidence
such as computer data, and psychological profiling of perpetrators. The digital trans-
formation, which also changes the nature and targets of crimes, in no way implies that
color analysis is losing its significance. Even in the digital realm, material properties
can be approximated using color information; it even offers new possibilities for the
analysis of light and color. For this reason, color analyses in both analog and digital
spaces will be presented below. Chapter 2 will delve into four specific methods,
elucidating the role of colors in the generation, recording, or interpretation of trace
materials. We commence with the analysis of biological evidence and culminate with
the scrutiny of digital traces.
2.1 Analysis of age-related color changes using the example of forensic bloodstain
age estimation
Within forensics, the analysis of blood traces constitutes a pivotal component in
crime resolution. It furnishes investigators with vital clues for perpetrator identifica-
tion and supplies information crucial for reconstructing the crime scene. The hue of
blood emerges as a prominent factor, particularly in blood age estimation, wherein it
serves as a basis for informed approximations regarding the age of blood droplets.
This segment elucidates the rationale behind color alterations and provides a succinct
overview of contemporary methods employed in blood color analysis.
2
Dye Chemistry –Exploring Colour From Nature to Lab
2.1.1 Properties of blood
Blood is often revered as the quintessential essence of life, symbolizing the endur-
ing vitality inherent in the human body. It is readily obtainable, and variations in its
constituent components serve as crucial indicators of an individual’s health status or,
in forensic contexts, the age of blood trails [4].
Functionally, blood supports and sustains various bodily processes by facilitating
the transport of essential substances. Mechanically propelled through the blood ves-
sels by the rhythmic contractions of the heart, it traverses from the heart via arteries
to organs and then returns via veins. On average, the human vascular system contains
approximately 70–80 ml of whole blood per kilogram of body weight. Comprising
roughly 55% blood plasma and 45% blood cells, including platelets, red blood cells,
and white blood cells, whole blood predominantly consists of red blood cells. Consti-
tuting around 96% of the blood’s mass, red blood cells (erythrocytes) serve as the
primary carriers of oxygen absorbed from the lungs to organs and facilitate the
transport of carbon dioxide from organs back to the lungs, thus facilitating cellular
respiration [4].
The red color of blood stems predominantly from the protein hemoglobin, which
constitutes the majority of red blood cell content. Hemoglobin comprises four sub-
units, each bound to an iron II complex known as heme. Central to the heme complex
is an iron atom capable of reversible binding to oxygen molecules.
In the circulatory system, hemoglobin exists either in its oxygen-enriched form,
oxyhemoglobin, or is deoxygenated during its journey back to the lungs as
deoxyhemoglobin. Exposure to atmospheric oxygen outside the body gradually
oxidizes oxyhemoglobin to methemoglobin, irreversibly converting the central
iron atom to its trivalent state and binding water in place of oxygen. This
oxidation process darkens the overall color of the blood. Over time, the
concentration of methemoglobin increases, ultimately resulting in complete
hemoglobin oxidation. As decay progresses, typically after 2–3 weeks, amino acids
such as histidine tightly bind to the central iron atom, leading to the formation of
hemichrome. This denaturation of blood pigment is accompanied by a color change
from brown red to dark red or black.
Upon exiting the body, a drop of blood undergoes a gradual transformation in
composition and subsequent color, transitioning from light red to deep black. The
pace of this metamorphosis is contingent upon external factors, such as surface prop-
erties, temperature, or humidity [5].
2.1.2 Spectroscopic analysis of color change
Spectroscopy is a biophysical measurement method extensively employed across
various forensic domains. It furnishes insights into the dimensions, configuration,
architecture, charge, molecular mass, functionality, and kinetics of scrutinized mac-
romolecules. This technique harnesses specific characteristics of light, enabling
deductions regarding the condition of the analyzed sample.
More specifically, spectroscopy encompasses a collection of physical methods
wherein the interactions between electromagnetic radiation and surrounding matter
are observed and quantified. These interactions are recorded in the form of spectra
using instruments called spectrometers. Spectra represent the intensity distributions
of radiation as a function of wavelength, aiding in the identification of the substance
under examination (Figure 1) [6, 7].
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Colors in Forensics: The Analysis and Visualization of Forensic Data and Evidence
DOI: http://dx.doi.org/10.5772/intechopen.1006108
The aforementioned changes in blood, attributed to varying hemoglobin deriva-
tives, can be monitored through blood spectra. These spectra exhibit evolving bands
contingent upon the age and prevailing hemoglobin derivative. Notably, the Soret
band, originating from the fundamental chemical structure of the heme group,
exhibits a maximum intensity at approximately 425 nm in young bloodstains. As
stains age, this peak progressively shifts toward the ultraviolet range, with blood
traces older than 3 weeks showcasing a Soret peak around 400 nm. Additionally,
young blood manifests peaks of oxyhemoglobin at 542 and 577 nm. However, as blood
ages, the proportion of methemoglobin increases, characterized by peaks at 510 and
631.8 nm. Over time, oxyhemoglobin peaks diminish in intensity within the overall
blood spectrum, giving way to methemoglobin peaks [8, 9].
In practice, forensic investigators collect blood samples from crime scenes and
subject them to spectroscopic analysis. The spectral bands obtained are then mathe-
matically compared with literature values to estimate the approximate age of the
bloodstains. A detailed procedural elucidation of this methodology can be found in the
chapter “Forensic Analysis of Bloodstain Color”from the book “Advances in Color-
imetry”by IntechOpen (Figure 2).
Figure 1.
Hemoglobin derivatives: the divalent iron present in oxyhemoglobin undergoes oxidation to trivalent iron, resulting
in the formation of methemoglobin, which loses its ability to transport oxygen. Subsequently, during denaturation,
histidine complexes frequently emerge, wherein the iron is fully bound, forming hemichromes. The distinct peaks
exhibited by these hemoglobin derivatives in the electromagnetic spectrum contribute to the transition of blood color
from light red to brown and ultimately to black (own illustration).
4
Dye Chemistry –Exploring Colour From Nature to Lab
Ultimately, it is imperative to underscore that spectroscopic recording of the age-
related chemical alteration in the central iron atom of the blood pigment hemoglobin
is conducted to estimate the forensically relevant timeframe during which the blood
traces have been present at the crime scene.
2.2 Analysis of electrochemically stimulated color changes using the example of a
novel method for detecting fingerprints
This subchapter offers insights into a new research field within the realm of
forensic dactyloscopy. Unique characteristics of the fingerprint were electrochemi-
cally stimulated, facilitating high-resolution capture of the trace and recording of
forensically pertinent features.
Dactyloscopy—Characteristics of fingerprints Dactyloscopy, derived from ancient
Greek (dáktylos “finger”and skopiá “peeping”) [11], pertains to the examination of
papillary ridges on the palms of hands and the soles of feet. The biometric method of
identity verification through dactyloscopy, commonly known as fingerprinting, is
based on the biological irregularities present in human papillary ridges.
Dactyloscopy has been utilized in forensic science for identification purposes since
the early twentieth century, making it the oldest biometric method. Its introduction in
Germany dates back to 1903 when Paul Koettig implemented it at the Dresden police
headquarters. A notable criminal case in 1914 involved the murders on
Hohlbeinstrasse and Terrassenufer in Dresden, where fingerprints left on a metal
cassette were decisively linked to seamstress Marie Margarethe Müller.
Francis Galton presented various skin ridge patterns and devised a classification
system, laying the groundwork for Edward Richard Henry’s pattern classification
system, which is still in use today. Fingerprinting captures an image of the papillary
Figure 2.
Blood spectra depending on the stain age: the spectra of blood stains spanning various ages (ranging from 0.1 to
1000 hours) are compared, with color grading reflecting the age progression. age-associated alterations in the
spectra are depicted at distinct positions, stemming from varying concentrations of hemoglobin derivatives
(modified from [10]).
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Colors in Forensics: The Analysis and Visualization of Forensic Data and Evidence
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ridges on the fingertips, with each individual having a unique fingerprint. Biologically,
a papillary ridge refers to an elevation in the epidermis on the palm or sole. Henry’s
classification system distinguishes various features of the fingerprint.
Figure 3 depicts one of the general patterns, anatomical features or “minutiae,”
and the sweat pore pattern, all of which can serve as identifying features in
dactyloscopy.
Physical, chemical, or hybrid methods are employed to preserve fingerprints, often
complemented by optical aids, such as oblique light, transmitted light, halogen, and
UV light, as well as magnifying glasses [12].
Forensics essentially always involves the comparison of trace materials. In reality,
this comparison is often hindered by the fact that the traces at the crime scene
inherently have or due to the method of collection exhibit low quality. Therefore,
methods are needed to preserve or improve this quality. Furthermore, it is always
advantageous if traces can be examined directly at the crime scene, as an analysis
following the prescribed “legal preservation”often leads to points of contention in
court. Both of these issues are intended to be addressed with the novel electrochemical
analysis method outlined below.
2.2.1 Electrochemistry in dactyloscopy
The electrochemical analysis method facilitates the generation of high-resolution
images of papillary ridges, thereby enabling the examination of sweat pores. This
significantly enhances the recording quality of fingerprints and the underlying ana-
tomical features. Furthermore, it opens up the possibility of translating sweat pore
assessment from fundamental research into practical forensic applications. With this
innovative technology alone, basic patterns, minutiae, and pores are displayed in high
resolution. The varying compositions of biomolecules on the fingerprint allow for
gender determination and differentiation between adults and children. Additionally,
insights into contact with chemical compounds such as explosives, drugs, or medica-
tion can be gleaned. Importantly, this method preserves the structural integrity of the
fingerprint.
The novel analysis method relies on an electrochemical reaction. Initially, the
fingerprint is mixed with non-destructive marker substances and then stimulated
(luminescence). The application of voltage induces the release of electrons from a
chemical compound to a reactant. The electronically excited marker substance subse-
quently returns to its baseline state by emitting light (luminescence). However, if
grease or sweat is present on the fingerprint carrier, the reaction is impeded.
Figure 3.
Levels of information from a fingerprint that is used for identification (own illustration).
6
Dye Chemistry –Exploring Colour From Nature to Lab
Consequently, only the spaces, that is, the finger grooves and pores, luminesce, create
a highly detailed negative image of the fingerprint.
Alternatively, a positive image can be generated by treating the fingerprint with
marker substances specific to the sought chemical compound (target substance).
Subsequent application of the reaction partner results in luminescence only in areas
where the desired compound is present. These may include narcotics, fire accelerants,
or explosives. Relevant drugs within the scope of criminal law, such as cocaine, heroin
(with the metabolite morphine or 6-monoacetylmorphine), codeine, methadone, and
tetrahydrocannabinol (THC), can also be detected using this method.
For a comprehensive fingerprint analysis using this technique, the marker sub-
stance must target amino acids as the desired substance, causing the entire fingerprint
to luminesce upon stimulation. Various substances can be employed for the basic
electrochemical reaction. While luminol sometimes led to vigorous bubble formation
on the trace, hindering analysis, ruthenium emerged as the ideal substance for the
foundational electrochemical reaction.
These developed solutions culminated in the creation of an initial integrated solu-
tion comprising a modified camera and electrolytic cell, capable of implementing the
desired functionalities. The high-contrast, high-resolution photos generated through
this method could already be examined in the prototype using suitable image
processing and evaluation programs (Figure 4) [14].
2.3 Image forensics
In recent years, there has been a significant shift toward digitalization worldwide.
Nature, too, can now be digitally portrayed in myriad shapes and colors. In the field of
image and video forensics, natural elements like people, landscapes, and objects are
digitally captured, scrutinized, and analyzed. The initial stages of forensic investiga-
tion, marked by analog methods such as detecting bloodstains during aging or visual-
izing evidence at crime scenes, were just the beginning. The future lies in digital
Figure 4.
From the idea to the prototype. The ECL imaging system, adapted from Xu et al., was utilized to electrochemically
visualize fingerprints. While luminol enables this visualization, the formation of pronounced bubbles poses
challenges for high-resolution trace evaluation. In contrast, analysis with the ruthenium complex proceeds
smoothly, without such issues. Consequently, this variant was selected for creating the prototype depicted below
(modified from [13]).
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Colors in Forensics: The Analysis and Visualization of Forensic Data and Evidence
DOI: http://dx.doi.org/10.5772/intechopen.1006108
technologies, offering nearly boundless possibilities in terms of color variation for
forensic examinations. The transition from analog to digital is often facilitated by
cameras, which capture the world as we perceive it, thereby forming the basis for
digital forensics, particularly in image and video analysis.
The capture of reality through digital devices also enables the recording of the
entire color spectrum. This means that image and video analysis can provide a detailed
description of individuals based on their color profiles.
However, cognitive comparison must be based on a forensic foundation, as the
discretization of space can lead to deviations that need to be accounted for.
2.3.1 How a digital camera works/digital image collection
The design of a digital camera is modeled after that of the human eye, with various
components of both systems carrying out similar functions.
Both the camera and the eye rely on external lenses to gather light, directing it onto
the light-sensitive areas of their respective systems to achieve focus. Like the pupil
and iris of the eye, the camera’s aperture adjusts to control the amount of incoming
light based on the surrounding lighting conditions. Finally, the process of converting
light into electrical signals by the light sensors in a digital camera mirrors the function
of cone and rod cells in the retina of the eye. These cells transform incoming light into
electrical impulses, which are then further processed [15, 16].
2.3.2 Image capture process
In the realm of photography, what the human brain intuitively controls in the eye
must be replicated through intricate engineering processes. The journey of informa-
tion from a subject in an image through the sensor system to a digital format involves
several complex steps (see Figure 5).
Figure 5.
From the object in real space to the object in virtual space. The light reflected by the real-space object is collected by
the camera lenses, projected onto the image sensor through a Bayer filter and thereby converted into digital signals,
which are subsequently saved as a digital image (own illustration).
8
Dye Chemistry –Exploring Colour From Nature to Lab
Initially, the light reflected from the image subject is gathered by the camera’s
outer lenses. These lenses are pivotal in concentrating incoming light and projecting a
sharp image onto the image sensor.
To achieve clarity in the image, the camera adjusts the distance between these
outer lenses to ensure the subject is in focus. Typically, this adjustment is managed by
a small motor, guided by measurements from an integrated distance-measuring
device. This computer-controlled autofocus mechanism significantly contributes to
producing clear, detailed, and non-blurry images.
At this juncture, the paths of the camera and the eye diverge. While the eye can
only focus on a small area of the image at a time, with the brain synthesizing the
overall scene from this limited information, the camera must simultaneously focus on
multiple pixels of the object [17].
The light collected by the outer lenses reaches the camera’s light sensor array,
comprising millions of tiny photodiodes (pixels) that convert incoming light into
electrical signals. To enhance image quality, each pixel is individually targeted by a
corresponding microlens, with the number of microlenses aligning with the pixel
count on the sensor. For instance, the iPhone 15 boasts a 48-megapixel camera,
indicating a sensor array equipped with over 40 million lenses alone.
The functioning of a pixel resembles that of a photovoltaic cell, measuring the
intensity of light it receives. This analog measurement (brightness) is then converted
into a digital signal (brightness value). These sensors respond to light in the wave-
length range of 190–1100 nanometers. This range encompasses ultraviolet (UV) and
infrared (IR) light. Sensor sensitivity varies with material; while silicon sensors, com-
mon in most cameras, primarily respond to visible light, they can also react to other
wavelengths, potentially causing visual noise if filtering is inadequate [18].
Since the light sensor detects the entire visible spectrum, filtering is necessary to
capture not just brightness but also specific color values. This is achieved through
millions of color filters distributed across the pixels, commonly organized according to
the Bayer filter pattern [19]. The Bayer filter allows each pixel to capture only one
primary color (red, green, or blue), enabling recording of both color and grayscale
information. The Bayer filter arrangement is depicted in Figure 5.
Interpolation, also known as demosaicing or debayering, determines the color
values of individual pixels on the image sensor. This process fills in missing color
values through calculations involving neighboring pixels, resulting in each pixel hav-
ing its own red, green, and blue values regardless of its original detection color.
However, interpolation may introduce artifacts such as blurred edges or color
mixing, particularly in patterned subjects like a picket fence [20].
The outcome of this process is a RAW image, highly inefficient in terms of storage.
Compression is often employed to mitigate storage requirements, albeit at the risk of
data loss and image quality degradation.
2.3.3 Compression
To address the inefficiency of storing RAW images, compression algorithms like
JPEG are commonly employed. The abbreviation “JPEG”stands for “Joint Photo-
graphic Experts Group,”the organization behind this standard. The JPEG compres-
sion algorithm reduces image file sizes by eliminating redundant information and
compressing image data without perceptible loss in quality to the human eye. This
process involves techniques such as discrete cosine transformation (DCT) and
quantization [21].
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Colors in Forensics: The Analysis and Visualization of Forensic Data and Evidence
DOI: http://dx.doi.org/10.5772/intechopen.1006108
However, compression involves loss of information (“lossy compression”),
particularly in areas with intricate detail or low contrast. This can lead to visible
artifacts like blocking or blurring, especially in heavily or multicompressed images.
Such artifacts are scrutinized in digital forensic analyses, particularly in detecting
image forgery [22].
AI-assisted methods, especially in restoring images compressed multiple times
with JPEG, add complexity to digital forensic analysis, a practice known as
antiforensics [23].
2.3.4 Color representation in digital space
A digital image consists of a finite number of discrete elements called pixels. Each
pixel is identified by a specific, predefined position within the image and holds one or
more finite, discrete values [24].
Mathematically, a digital image (S) is commonly represented by a matrix
S = (s(x, y)). The rows (L) of this matrix are known as image rows, and the columns
(R) are referred to as image columns. The individual elements within this matrix
correspond to pixels. The variable x ∈{0, 1, 2, …,L1} denotes the index of the
respective row, while the variable y ∈{0, 1, 2, …,R1} denotes the index of the
corresponding column indicates. Thus, s(x,y) represents the value of a pixel at
the associated location coordinates [25].
2.3.5 Digital color spaces
In the simplest color space, each pixel contains only one intensity value,
resulting in grayscale images. These images are represented as a two-dimensional
matrix S = (s(x, y)), where each matrix element is assigned a value from a set of gray
values G, typically G = {0, 1, 2, …, 255}. Alternatively, values can be assigned to an
interval from 0 to 1, where 0 represents black and 1 represents white by default [25].
In the context of color images with multiple channels, each pixel is represented not
by a single value but by several color channels (N). Thus, s(x, y) becomes an
N-dimensional vector. Multichannel images are represented by a three-dimensional
image matrix S(s(x, y, n)), where the new variable specifies the index of the
respective image channel [25].
The RGB color space (see Figure 6, left), one of the most well-known, is depicted
as a unit cube in a three-dimensional Cartesian coordinate system. It is based on the
primary colors red, green, and blue. The grayscale values range along the main
diagonal from (0, 0, 0) for black to (255, 255, 255) for white [24].
This representation maintains the additive color mixing property, where white can
be achieved through the maximum intensity of mixing red, green, and blue. Any
other color can be represented by a combination of these primary colors. The
mathematical representation, assuming each primary color represents a channel (n),
would be s(s(x, y, 3)) with s(x, y) = (gRed, gGreen, gBlue) (modified from [25]).
Another prominent color model is the HSV color model (see Figure 6, right),
where H represents hue, S represents saturation, and V represents intensity, each
ranging from 0 to 1. This model is often visualized as an inverted hexagonal cone,
where (gH, 0, 0) represents black and (gH, 255, 0) represents white [24].
An advantage of the HSV color space is the ability to analyze and compare hue
independently of brightness and saturation, which is beneficial for forensic analysis.
10
Dye Chemistry –Exploring Colour From Nature to Lab
2.3.6 White balance
Color shifts within the pixel color values of an image can result in the image
appearing tinted, often dominated by a particular color. This effect is particularly
noticeable in night shots, where the orange light from street lamps influences the
captured image, causing a shift toward an orange color range. To address this issue,
the white balance method is employed. While the human eye can adapt to changes in
lighting and resulting color shifts automatically, cameras often struggle with this
adjustment. Professional photographers often use color charts with known color
values to precisely correct color shifts. Without such reference charts, achieving
accurate white balance can be challenging and may only be approximated.
One method used for white balance adjustment is the “Gray-world algorithm.”
This method assumes that the average pixel value in an image is neutral gray (with a
pixel color value of 128 in the RGB color space, represented as (128, 128, 128)) due to
the normal distribution of colors. By analyzing the average color of the image, this
algorithm estimates and corrects the color shift in the appropriate direction [26].
2.3.7 From the color to the case
Digital colors indeed hold significant importance in our daily lives, extending
beyond the realm of multimedia. They also play a crucial role in casework, particularly
in the development of specific evidence during criminal proceedings. For instance,
Figure 6.
RGB and HSV color model. Left: the RGB color space is depicted as a cube. In the top left, there is an RGB cube
showing black in the bottom back corner and demonstrating the addition of RGB values. In the bottom left, another
RGB cube is shown with white in the top, front corner, illustrating the subtraction of RGB values. Right: the HSV
color space is represented as an inverted cone with six edges. The middle arrow indicates the intensity value,
increasing in the direction of the arrow. Saturation is denoted by the lower arrow pointing to the right, with
saturation increasing in the direction of the arrow. The color is described by a circle (own illustration).
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Colors in Forensics: The Analysis and Visualization of Forensic Data and Evidence
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colors can be utilized forensically to support various facts by comparing different hues
to a desired color. Moreover, the ability to “make colors visible”is not merely fiction
in certain cases.
2.3.8 Video analysis from rival motorcycle clans
The video material from Eisenbahnstrasse in Leipzig in 2017 depicts a volatile
situation where two groups, identified by their distinctive black jackets, converge. The
groups move toward each other, leading to a confrontation involving physical contact
and gunfire. One individual is seen falling to the ground and remaining motionless.
These groups are identified as the United Tribunes and the Hells Angels.
The footage, captured by a passer-by using a smartphone camera, suffers from
poor quality and unstable camera work.
The initial question raised by the video material is: Which group entered the frame
from the right, and which from the left?
The various groups are depicted in Figure 7, where a section of the image displays
the logos on their jackets, albeit unclearly. Upon examining the original logos, it is
difficult to definitively answer the question. However, in Figure 8, both logos are
displayed clearly and scaled down to match the resolution of the image.
Simply comparing the shapes of the pixelated logos with the image sections taken
may not provide sufficient information, but analyzing the colors can offer more
insights. The standard Hells Angels logo incorporates the colors yellow and red.
However, in the cellphone camera footage of the incident, various shades of gray are
Figure 7.
Image sections from the video material, left-sided and right-sided grouping. The top image shows the left-sided
group with an enlarged section of the group logo on a member’s jacket. The bottom image shows the second group,
also with an enlarged section of the corresponding group logo. The quality of the image sections is poor, making it
impossible to clearly assign the persons to the groups represented (own illustration).
12
Dye Chemistry –Exploring Colour From Nature to Lab
predominantly visible. Due to the low quality and resolution, the colors of individual
areas are highly correlated, resulting in many pixels appearing gray. Nevertheless, the
color spectra can illustrate the distribution and appearance of colors.
Figure 8.
Logos of the “Hells Angels”and “United Tribuns”groups. In comparison, here are the respective logos of the groups
as they can be found on jackets. Image (a) shows the Hells Angels logo, which is scaled down to the quality of the
video material to produce image (b). Image (c) corresponds to the logo of the United Tribuns. If this is scaled down,
the result is image (d). The qualitative scaling is done to make the logos comparable with the footage from the video
(own illustration).
Figure 9.
Color spectrum analysis of the jacket logos from the video material. The application of the color spectrum analysis
provides information about the color distribution of the corresponding image section. The representation of the
individual color channels is listed here. The logo of the left-hand group produces the individual spectra on the left-,
the right-hand diagrams correlate with the logo section of the right group (own illustration).
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Colors in Forensics: The Analysis and Visualization of Forensic Data and Evidence
DOI: http://dx.doi.org/10.5772/intechopen.1006108
In Figure 9, the color spectra of both group logos are displayed. To achieve this, a
section of the respective image was selected, focusing solely on the logo without
including the surroundings or other objects in the analysis.
The color spectrum of the logo belonging to the group on the left exhibits a broad
distribution across the colors red, green, and blue, ranging from less intense colors
(appearing as black) to highly saturated areas. In contrast, the spectrum of individual
colors in the logo of the group on the right extends less strongly into the intense color
range. The differing characteristics of these spectra indicate that the logo on the left
contains more color variation than the one on the right. Consequently, the spectrum
on the left could be attributed to the Hells Angels group, as their logo typically
incorporates colors as part of its standard design. Conversely, the logo of the group on
the right displays much less intense colors, with the distribution mainly concentrated
in the darker, black-appearing areas. Therefore, it could be associated with the United
Tribuns logo, which typically appears in black and white without additional color.
Despite the limited information provided by the poor-quality video material
regarding the colors of the logos, insights into color distribution can be gained through
a color spectrum analysis. This method facilitated the assignment of the groups to
their respective clans with a high degree of probability. It underscores the notion that
just because colors may not be perceptible to the human eye in an image does not
mean they are absent.
2.3.9 Identification of cars by color comparison from video material
In a scenario where a car drives on a road at night under low street lighting,
surveillance cameras positioned across the street record the scene. Can digital foren-
sics ascertain the color of the car and identify the make and model based on its paint
job, especially when multiple models are available?
Specialized methods are employed to address this challenge, beginning with a color
analysis of the car in the surveillance video. To accomplish this, one of the camera’s
Figure 10.
The analysis reveals detail of a moving car exhibiting a visually perceived red color. Color information was
extracted for a total of two pixels positioned around the image section. In both pixels, the red component is
dominant over the green and blue components, suggesting a red car (own illustration).
14
Dye Chemistry –Exploring Colour From Nature to Lab
images is loaded into an image processing program to extract color values. The analysis
step is depicted in Figure 10.
The figure illustrates that the red component of the image surpasses the green and
blue components. Color determination is not solely reliant on visual inspection but is
also documented using analytical methods. However, there are various shades of red
within the area of car painting. The subsequent step involves comparing the target
vehicle with other cars of similar colors to ascertain its specific shade. Based on previous
process steps, the focus has been on the Fabia vehicle type from the Skoda brand.
Figure 11 displays comparison models in various paint finishes within the red spectrum.
To generate meaningful comparison material, vehicles in the potential paint colors
were driven past the original camera at night. The resulting video footage provides a
more realistic basis for comparison. Upon comparing the underlying image or video
material from the night of the crime with the comparison material, several differences
emerge, including in the white balance. White values are closely linked to color
temperature and brightness, factors that influence how objects are perceived by the
human eye. For instance, cars may appear differently colored at various times of
the day due to factors such as the position of the sun and atmospheric conditions like
the refraction of sunlight by water molecules. The human brain naturally adjusts to
interpret objects, such as a white sheet of paper or the pages of a book, as white under
different lighting conditions.
However, a digital camera requires explicit instructions regarding the color tem-
perature it should use, a process known as white balance. This adjustment was applied
to the comparison video material.
Following adjustments to the video materials, the datasets of all comparison vehi-
cles were prepared for comparison. Color matching of all vehicles with the original
video material was conducted using a specialized image processing algorithm and
quantitative assessment of color assignment. The results of these analyses, including
correlations and differences, were summarized using an algorithm, with Figure 12
presenting the results graphically.
The diagram illustrates a notably closer match between the Corrida Red paint color
and the vehicle involved in the crime compared to the other types. Through the
utilization of specialized color comparison algorithms and the subsequent analysis
connecting them, it can be inferred with a high probability that among all the com-
pared paint colors, Corrida Red is the one most likely present on the crime vehicle.
Figure 11.
Comparison models––depiction of several car types from the manufacturer Skoda, showcasing the comparison
colors Corrida Red, Flamenco Red Metallic, Orange Tangerine Metallic, Rio Red, and Red Rosso Brunello Metallic
(own illustration).
15
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DOI: http://dx.doi.org/10.5772/intechopen.1006108
2.3.10 The gold coin and the shoe
On March 27, 2017, the infamous Remmo clan stole the 100 kg gold coin “Big
Maple Leaf”from the Bode Museum in Berlin. As part of the planning of this auda-
cious theft, various individuals were captured on surveillance footage at Hackescher
Markt a few days before the crime. Among them was a person wearing highly dis-
tinctive shoes, which bore a striking resemblance to a pair of shoes identified by the
police during their investigation (see Figure 13).
Figure 12.
The summary of all analysis data presents a comprehensive overview of correlations and differences between the
comparison model images and the crime vehicle image. The shape of the bars in the visualization indicates the
similarity of each comparison model image to the image of the crime vehicle. This allows for a clear understanding
of the degree of resemblance and helps in identifying potential matches (own illustration).
Figure 13.
(1) Suspicious person with distinctive shoes; (2) LKA officer with seized shoes (own illustration).
16
Dye Chemistry –Exploring Colour From Nature to Lab
The pattern of the comparison shoe (Figure 13, middle) precisely matches the
pattern on the suspect’s shoes (Figure 13, bottom left). However, a red stripe is clearly
visible on the comparison shoe, which is not apparent in the video material. To
investigate whether they are the same pair of shoes despite this discrepancy, compar-
ative photos of the shoes worn by an LKA officer were captured using the same
surveillance camera at the same location. This examination revealed that the surveil-
lance camera loses some color information during recording and that, under these
conditions, the comparison shoes at least appear similar to the suspect’s shoes.
To quantify the color changes caused by the surveillance camera, two umbrellas
were set up at Hackescher Markt and filmed: one multi-colored and one white. The
same umbrellas were also photographed under studio conditions in a photo laboratory.
These images were then compared for color and brightness values (see Figure 14).
This investigation unveiled that the surveillance camera inadequately captures red
tones in recordings (see Figure 14, segments 2 and 4).
In this instance, it becomes evident how different camera models perceive colors
differently. Accuracy in coloring is often not the primary concern with surveillance
cameras, as it is often compromised in favor of considerations such as storage space,
frame rates, or resolution. This fact frequently comes into play, particularly in corre-
spondence analyses.
2.4 Artificial light sources as a source for detecting further forensic aspects
The preceding chapters have focused on the illumination of light sources and the
utilization of artificial light and color sources. However, light itself can contain
Figure 14.
(1, 2) Umbrellas at Hackerscher Markt; (3, 4) Umbrellas in the photo studio (own illustration).
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additional information that holds forensic value, particularly when artificial light
sources are recorded. These recorded light sources can offer insights into the
authenticity or chronology of the recordings. The examination of artificial light
sources for forensic interpretation relies on signal processing methods used to analyze
light fluctuations.
2.4.1 Creation of artificial light
Light comprises electric and magnetic fields that propagate in waves, constituting
electromagnetic radiation or electromagnetic waves. Typically represented as a sine
wave, light is characterized by wavelength, frequency, amplitude, and phase. The
human eye perceives colors within the wavelength range from approximately 380 nm
(violet) to 780 nm (red) [27].
Artificially generated light, termed lighting, is produced through energy conver-
sion. Specifications of a lamp include the color temperature, measured in Kelvin (K),
and the luminous efficacy, expressed in lumens per watt (lm/W) [28].
Fluorescent lamps operate on the principle of gas discharge and fluorescence
technology. The energy supplied ionizes mercury atoms within the lamp, causing
collisions with electrons, which emit ultraviolet radiation. This radiation is
absorbed by a fluorescent coating inside the lamp, converting it into visible
fluorescent light [29].
Due to phase changes in the AC network, the light intensity of artificial lighting
fluctuates between maximum intensity and an off state. This fluctuation manifests as
flickering at twice the main frequency, typically around 100 Hz in the European
energy network [30].
2.4.2 European interconnected network
The public supply of electrical energy in Europe is facilitated by an interconnected
system, comprising spatially neighboring and electrically connected transmission net-
works. Coordination and monitoring of this interconnected system are overseen by
the association ENTSO-E (European Network of Transmission System Operators for
Electricity) and its member transmission system operators [31]. As of March 2022, 36
European countries were integrated into this network system.
Since electrical energy cannot be stored sufficiently, energy consumption must
align with production. Any imbalance between energy production and consumption
leads to frequency deviations from the nominal frequency (fNenn) of 50.0 Hz in the
power grid. The primary cause of these deviations is block-by-block electricity trad-
ing, although demand fluctuations also influence grid frequency. These deviations are
constrained within a range of 200 mHz (millihertz), corresponding to a normal
operating range of 49.80–50.20 Hz. The frequency and amplitude of these deviations
vary depending on the time of day or region. For instance, strong positive frequency
deviations are typically observed before 5 a.m., while significant drops in network
frequency often occur in the evening.
Moreover, due to the 15 or 30-minute trading intervals in the European electricity
grid, particularly pronounced fluctuations are expected between these intervals.
There is a recurring pattern in the network frequency, characterized by a sharp drop
at the start of a 15 or 30-minute interval, followed by an increase until the interval’s
conclusion [32].
18
Dye Chemistry –Exploring Colour From Nature to Lab
2.4.3 Network frequency analysis
Network fluctuations in the nominal frequency can be assessed using Electric
Network Frequency Analysis (ENF). One of the pioneering works on ENF, dating
back to 2005, is by Grigoras [33], which addresses network frequency artifacts in
audio files recognized as forensically significant. The ENF criterion primarily involves
measuring the mains frequency at a power source to establish a reference data set,
extracting the ENF from audio material, and subsequently comparing both ENF
tracks.
Initially, the research primarily focused on applications in the field of audio foren-
sics. Grigoras’approach outlines the extraction of an ENF track from audio recordings
for timestamp verification. This involves downsampling to 120 Hz and band-limiting
the audio signal to a frequency range of 49–51 Hz. The resulting ENF track is then
visually compared with a reference database using Fast Fourier Transform (FFT)
techniques [33].
Establishing a reference database for future analysis is crucial for classifying
recorded ENF tracks. This entails selecting diverse measurement locations to capture
frequency curves and reference data from various network areas.
2.4.4 Artificial light sources in videos
Video recordings from surveillance cameras, smartphones, digital cameras, and TV
recordings are predominantly audiovisual, capturing both video images and accom-
panying audio tracks simultaneously. Additionally, digital video recordings are char-
acterized by several parameters. These include:
1.Frame rate: the frame rate, measured in Hertz (Hz) or frames per second (fps),
indicates the number of video frames recorded per unit of time.
2.Image size: this refers to the dimensions of the video frame, typically specified in
terms of vertical pixels (columns) and horizontal pixels (rows), which determine
the resolution of the video.
3.Color depth: the color depth specifies the number of color channels in an image,
indicating the maximum number of possible color and brightness values per pixel.
The combination of these characteristics defines the video format of a recording. It
is important to distinguish the video format from the data format used, which deter-
mines the technical structure and interpretability of the digital video data during
processing. Typically, the data structure is organized as a container format containing
separate audio and video content. This structure describes the compression methods
or algorithms, known as codecs, used for encoding and decoding the audio and video
content (Figure 15).
The global electronic shutter mechanism used here is called a global shutter.
Compared to complementary metal-oxide-semiconductor (CMOS) sensors,
charge-coupled device (CCD) sensors are more sensitive to light, have lower noise,
and have a lower image readout rate. With CMOS sensors, pixels are exposed line by
line from top to bottom and converted directly into a voltage signal and read out by an
electronic circuit for each pixel element [31].
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The “rolling”shutter mechanism used, known as a rolling shutter, increases the
image readout rate, but the line-by-line scanning also represents a source of error, for
example when recording fast-moving objects. If the entire pixel area is scanned line or
column by line slower than the movement of the recorded object, a distorted image is
created due to the time delay in scanning from the first to the last line. This error is
called the rolling shutter effect [35].
2.4.5 ENF in imagery and “white wall”method
Through the analysis of audio components in video recordings, an Electric
Network Frequency (ENF) analysis can also be conducted for video files. In
cases where the audio track is absent or unsuitable for ENF analysis, the video
content captured under artificial lighting can serve as an alternative. Garg et al.
presented a method in [29] 2011 to detect ENF using optical sensors and video
cameras. This approach broadened the scope of ENF forensic investigations to
encompass the examination of image material within video files. Studies by
Garg et al. in [29] revealed that fluctuations in light intensity, caused by emissions
from fluorescent lamps commonly used in indoor lighting, are directly correlated
with ENF.
Garg et al. introduced the “white wall”method, wherein a white wall serves as
the constant backdrop of a video and is illuminated by a fluorescent lamp. The average
intensity of video frames over a 10-minute period was calculated, and a bandpass
filter with a frequency band of 0.5 Hz around the calculated “aliased”frequencies
fa was applied. Subsequent bandpass filtering revealed the ENF signal in
spectrogram displays. Spectrogram analysis was conducted using the short-time Fou-
rier transform, with parameters set to a window size of 480 video frames (approxi-
mately 16 seconds) and an overlap factor of 50%, facilitating the detection of ENF
signals [29].
The results reported by Garg et al. underscore the necessity of calculating aliased
frequencies f
a
due to discrepancies between the fluorescent lamp’s light flicker fre-
quency (f
l
≈100 Hz) and the camera’s sampling rate (e.g., f
s
= 30 fs = 30 Hz). This
mismatch violates the sampling theorem, resulting in aliasing effects. To translate
Figure 15.
How (a) CCD sensors and (b) CMOS sensors work (own illustration based on [34]).
20
Dye Chemistry –Exploring Colour From Nature to Lab
recorded video intensities from the time domain to the frequency domain, Fourier
transformation is employed. However, since Fourier transformation can only detect
frequencies between 0 and the Nyquist rate (f
Nq
), aliased frequencies f
a
must be
determined for the light flicker frequency of 100 Hz [36].
The formula below, using the nominal light flicker frequency of 100 Hz and the
camera’s sampling rate is employed to calculate aliased frequencies [37]:
fa¼∣flkfs∣≤ fs
2,∃k∈(1)
The aliased frequencies indicate the frequency ranges within which the ENF track
can be localized [38].
The occurrence of the rolling shutter effect, associated with CMOS camera sensors,
further affects the accuracy of ENF analysis detection. A problem arises due to time
delays affecting the sequential exposure and reading of image material [39]. Experi-
ments with various cameras revealed that an idle period follows the sequential
line-by-line reading of video images before the next video image is read out [40].
Consequently, the ENF signal is not continuously captured. It was also observed that
sequential image capture can contribute to an increase in the effective sampling rate,
as multiple points in time are captured within a single video image [39]. This directly
impacts the camera sampling rate of CMOS-based devices.
Adjustments to the sampling frequency are necessary to account for this
phenomenon [41]:
fscmos
¼F1hfs(2)
Examining the formula, it becomes evident that the product of the height F
1h
(in
pixels) of a video image and the video frame rate f
s
(in fps) yields an increased
sampling rate fscmos for videos recorded with CMOS sensors [41].
2.4.6 Comparison of network frequencies with databases
A comparison of the data between the recorded reference data and the data deter-
mined from the audio or video content can be performed by calculating the correla-
tion coefficients. The correlation coefficient is a measure used in signal processing to
quantify the similarity between two spatial or temporal functions, X and Y.
Depending on whether the correlation coefficient is closer to +1 or 1, it indicates a
positive or negative correlation between the data series. A positive correlation sug-
gests that the measured values in the comparison file and the original file increase or
decrease simultaneously. Conversely, if the correlation coefficient is closer to 0, it
indicates little or no correlation.
To compute the correlation, the two signals under consideration, the one from the
reference database and the ENF signal recorded from the video, are compared. To
determine the time of recording in the reference data set, the shorter signal, the
network frequency in the video recording, is systematically shifted relative to the
longer signal, the network frequency of the reference database. The peak of the
correlation function then indicates the maximum similarity between both signals.
Establishing the temporal reference of the network frequency in the reference
recording enables the classification and determination of the recording time of the
video, akin to a forensic timestamp of a video file.
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Colors in Forensics: The Analysis and Visualization of Forensic Data and Evidence
DOI: http://dx.doi.org/10.5772/intechopen.1006108
The forensic utility of ENF analysis can be categorized into the following areas of
application:
1.Determination of recording times through temporal analysis of reference data.
2.Identification of the recording locations by correlating different network
connection areas.
3.Assessment of continuity/authenticity by identifying the presence of ENF signals.
2.4.7 Conclusion
With the increasing prevalence of video recordings generated using artificial intel-
ligence or manipulated multimedia data, such as deepfakes, ENF analysis emerges as a
crucial tool for verifying their authenticity [42]. Detecting deepfakes and other
manipulations of audiovisual media files is essential in the fight against terrorism [43].
Additionally, these techniques are invaluable for solving various criminal acts related
to video material. Examples include crimes involving ransom demands, kidnapping
scenarios, attacks on surveillance systems, and the widespread distribution of child
pornography video files [43].
3. Discussion and outlook
The discussed methods underscore the importance of color analysis and color itself
in the reconstruction of criminal events, as well as the identification of suspects and
crime-relevant objects. Color analysis is applied in various contexts, such as estimating
the age of bloodstains and linking suspects to crime scenes. Similarly, suspect identi-
fication is aided by quantifying color changes in surveillance cameras and analyzing
color spectra in logos. The classification of cars based on colors and their comparison
in videos is also a significant application area.
Furthermore, the significance of colors in case processing in criminal proceedings
is highlighted, with various facts being forensically supported through color matching.
It is evident that different camera models perceive colors differently, emphasizing the
need to consider this in forensic analysis.
An essential aspect to consider is the need for a standardized approach to color
analysis in forensic investigations. Given the relatively new methods for analyzing
digital color phenomena, there is often a lack of a gold standard or comparison values
for satisfactory evaluation. Large-scale studies on data collection and analysis could
enhance accuracy and comparability in the future. Additionally, the development of
guidelines and best practices for color analysis in forensics could improve the quality
and reliability of investigations.
It has been demonstrated that light itself can contain additional information with
forensic value, especially when artificial light sources are recorded.
In summary, this work illustrates the diverse applications of color analysis in
forensics and demonstrates how colors can support various forensic methods in
drawing meaningful conclusions from recorded data. Continued advancements in
technologies and methods for color analysis, coupled with the establishment of stan-
dards and best practices, will play a vital role in further advancing forensic science and
aiding in crime-solving efforts.
22
Dye Chemistry –Exploring Colour From Nature to Lab
4. Conclusion
Color and forensics are two inseparable fields. The information generated from
color-based investigations and analyses is sometimes significant for various areas of
analog and digital casework. Electromagnetic light allows insights into the real course
of events in many ways and supports the task of truthfully reconstructing events. In
particular, its applicability in the digital realm, for example during video analysis,
supports our legal system on a new level. The digitalization of our everyday lives will
continue to advance, and forensics will follow suit. The medium of color will also play
an indispensable role in its relevance for digital forensic analyses.
Conflict of interest
The authors declare no conflict of interest.
Author details
Tommy Bergmann*, Ronny Bodach, Laura Pistorius, Svenja Preuß, Paul Seidel and
Dirk Labudde
Forensic Science Investigation Lab (FoSIL), University of Applied Sciences Mittweida,
Germany
*Address all correspondence to: bergmann@hs-mittweida.de
© 2024 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of
the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided
the original work is properly cited.
23
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DOI: http://dx.doi.org/10.5772/intechopen.1006108
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Dye Chemistry –Exploring Colour From Nature to Lab
