# Visibility Assessment of Latent Fingerprints on Challenging Substrates in Spectroscopic Scans

Mario Hildebrandt, Andrey Makrushin, Kun Qian, Jana Dittmann

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Mario Hildebrandt, Andrey Makrushin, Kun Qian, Jana Dittmann. Visibility Assessment of Latent Fingerprints on Challenging Substrates in Spectroscopic Scans. 14th International Conference on Communications and Multimedia Security (CMS), Sep 2013, Magdeburg,, Germany. pp.200-203, 10.1007/978-3-642-40779-6_18 . hal-01492822

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Distributed under a Creative Commons Attribution 4.0 International License
# Visibility Assessment of Latent Fingerprints on
Challenging Substrates in Spectroscopic Scans

Mario Hildebrandt, Andrey Makrushin, Kun Qian, Jana Dittmann

Research Group on Multimedia and Security, Otto-von-Guericke University of Magdeburg,
Universitaetsplatz 2, 39106 Magdeburg, Germany
{hildebrandt, makrushin, kqian, dittmann}@iti.cs.uni-magdeburg.de

**Abstract.** Our objectives for crime scene forensics are to find the substrates on which finger traces are visible in limited ranges of the electromagnetic spectrum using UV-VIS reflection spectroscopy and to determine the optimal ranges in the interval from 163 to 844 nm. We subjectively assess the visibility of fingerprints within detailed scans with a resolution of 500 ppi and compare the results with those of an automatic visibility assessment based on the streakiness score. Ten different substrates are evaluated, each with three fingerprints from different donors. Streakiness score is confirmed to be a suitable fingerprint visibility indicator on non-structured substrates. We identify two substrates, namely metallic paint and blued metal, on which ridge lines become visible exclusively in UV range from 200 to 400 nm and from 210 to 300 nm correspondingly.

**Keywords:** Forensics, latent fingerprints, UV-VIS spectroscopy, fingerprint visibility assessment.

## 1 Extended Abstract

As the most common form of trace evidence left at crime scenes, latent fingerprints pose the most challenges for forensic experts [1]. Since they are present rather often yet invisible unless brought out with chemical and/or physical processes designed to enhance the visibility of fingerprint residues [2] (e.g. fuming, powdering, or deposition of chemical reagent), it is important to identify substrate dependent technique or reagent and the appropriate processing sequence for successful enhancement.

In today's crime scene forensics three kinds of substrate properties are known [2][3]: 1) porous substrates (e.g. paper) are absorbent and provide good adhesion for latent fingerprints; 2) non-porous substrates (e.g. glass) do not absorb, so latent fingerprints on such substrates are more susceptible to damage; 3) semi-porous substrates (e.g. glossy cardboard) both resist and absorb fingerprint residue, so it may or may not soak in depending on its viscosity as well as on the absorbent properties of the substrates. Since substrates have different characteristics regarding the absorption of fingerprint residues and light reflection, different fingerprint development techniques are required. On challenging substrates these techniques have an invasive na-
ture as they involve applying alien substances on the fingerprint residue, thus causing
the potential risk of compromising the evidence. Hence, various non-invasive ap-
proaches are developed to visualize latent fingerprints, e.g. by using various sensors
and light sources [4], or Chromatic White Light (CWL) sensors [3][5][6]. It is com-
mon that substrates reflect specular light differently depending on the wavelength of
the light source. This effect can be used to render fingerprint residues visible and to
acquire them in detailed scans. However, inherent properties of substrates (e.g. trans-
lucency or structure) might interfere with the scanning, making the identification of
the substrate of crucial importance.

We apply the spectroscopic acquisition with a lateral resolution of 500 ppi in the
range from 163 to 844 nm on latent fingerprints deposited on ten substrates and then
assess the fingerprint visibility in those detailed scans subjectively and by calculating
the streakiness scores [5].

In the subjective visibility evaluation of the fingerprint a label corresponding to its
suitability for forensic investigations is assigned. In particular, we distinguish be-
tween four classes of fingerprint visibility: 1) invisible, i.e. fingerprint is not percepti-
ble for the naked eye; 2) weak pattern, i.e. fingerprint is partially visible but not usa-
ble for forensic investigations; 3) fair pattern, i.e. fingerprint is visible but covered by
the substrate’s texture or structure, and this usually requires longer investigation time;
4) perfect pattern, i.e. fingerprint can be easily segmented using binarization.

The streakiness score S [5] is used as the objective visibility measure. However, in
contrast to [5] we do not use substrate specific filters therefore achieve fully automat-
ic processing. The basic idea is assigning S to determine the visibility of the finger-
print at each particular wavelength. Since S is the relative number of pixels in ridge
lines in a binarized fingerprint pattern, its value rests in the interval [0, 1], where 0
denotes a completely invisible fingerprint pattern and 1 a perfectly visible one. It
relies on the assumption that a fingerprint forms a striated pattern consisting of local
ridge-valley patterns with constant frequencies and directions. A preprocessing in the
Fourier domain is applied to enhance the ridge-valley pattern, afterwards an en-
hancement based on the approach from [7] is applied. The first step consists of mul-
tiplying the Fourier representation with a ring filter around its origin (see [8]) fol-
lowed by a zero-mean normalization. The second step consists of estimating local
orientations and a corresponding reliability map, both based on gradients. Further-
more, local frequencies are determined for reliable orientations. This is the foundation
for locally applying corresponding Gabor filters for the enhancement of the ridge
pattern. At last, the filtered image is binarized to calculate the streakiness score.

As a benchmark, we adapt the concept of differential images from [6] to eliminate
possible interference introduced by the substrate and compare the visibility of finger-
prints on original and differential images. On cooperative substrates the fingerprint
visibility on original and differential images is expected to be equal. On challenging
substrates with a constant reflection the originally invisible fingerprints become visi-
ble in differential scans. On challenging substrates with a random reflection the visi-
bility is not expected to increase. The original approach subtracts the data from the
same area of the substrate without a fingerprint. In our proposed approach, the image
with the fingerprint is divided by the values from the images without the fingerprint:
The intensity image $I(\lambda_w)$ at one particular wavelength $\lambda_w$ in the interval [163, 844]nm with $w \in [0, 2047]$ can be determined by $I(\lambda_w)=R(\lambda_w) \cdot S(\lambda_w) \cdot Sens(\lambda_w)$, where $R(\lambda_w)$ is the reflection of the measured material, $S(\lambda_w)$ is the spectral distribution of the light source, and $Sens(\lambda_w)$ is the sensitivity of the spectrometer. Then $R(\lambda_w)$ can be divided into the reflection of the substrate $R_s(\lambda_w)$ and the specific absorption and fluorescence of the fingerprint residue $R_f(\lambda_w)$. By performing a division of $I(\lambda_w)$ by the spectral data from a reference scan $I_{ref}(\lambda_w)$, $S(\lambda_w)$ and $Sens(\lambda_w)$, as well as $R_s(\lambda_w)$ are eliminated, leading to an enhanced contrast.

In total we evaluate ten substrates that are common at crime scenes as summarized in Table 1, each with three fingerprints from different donors. The substrates are divided into the classes cooperative, moderate and challenging based on observations for the CWL sensor [6]. On cooperative non-structured and non-textured substrates we can assume the following ranges streakiness scores $S$ for the subjective visibility classes: $S<0.1$ indicates an invisible fingerprint, $0.1<S<0.3$ usually corresponds with a weak pattern, $0.3<S<0.5$ indicates a fair pattern which probably contains smeared areas, $S>0.5$ indicates a good to perfect fingerprint visibility. However, since we do not apply substrate specific filters those ranges do not apply on moderate and challenging substrates because higher values of streakiness scores are likely to be caused by surface properties.

<table><caption>**Table 1.** Substrates, integration times and subjective evaluation results of original and differential images (*** - perfect, ** - good, * - weak, and o - invisible fingerprint pattern)</caption><thead><tr><th rowspan="2">Substrate</th><th rowspan="2">Integration time (ms)</th><th colspan="2">Original image</th><th colspan="2">Differential image</th><th rowspan="2">Comparison to CWL [9]</th><th rowspan="2">Substrate property</th></tr><tr><th>Wavelength range (nm)</th><th>Quality</th><th>Wavelength range (nm)</th><th>Quality</th></tr></thead><tbody><tr><td>White furniture surface</td><td>100</td><td>163-844</td><td>***</td><td>163-844</td><td>***</td><td>identical</td><td>non-porous, cooperative</td></tr><tr><td>Metallic paint</td><td>100</td><td>200-385</td><td>***</td><td>195-385</td><td>***</td><td>better</td><td>non-porous, challenging</td></tr><tr><td>Glass</td><td>50</td><td>210-710</td><td>***</td><td>200-844</td><td>***</td><td>identical</td><td>non-porous cooperative</td></tr><tr><td>Copy paper</td><td>250</td><td>220-250</td><td>*</td><td>220-250</td><td>**</td><td>identical/better</td><td>porous, challenging</td></tr><tr><td>5 Euro cent coin</td><td>100</td><td>220-520</td><td>***</td><td>220-844</td><td>***</td><td>identical/better</td><td>non-porous cooperative</td></tr><tr><td>Blued metal</td><td>500</td><td>210-300</td><td>*</td><td>210-300</td><td>**</td><td>better</td><td>semi-porous, challenging</td></tr><tr><td>Non-metallic paint</td><td>250</td><td>163-844</td><td>O</td><td>200-220</td><td>*/**</td><td>identical/worse</td><td>non-porous, challenging</td></tr><tr><td>Brushed stainless steel</td><td>100</td><td>163-844</td><td>*/**</td><td>210-270</td><td>**</td><td>worse</td><td>non-porous, moderate</td></tr><tr><td>Beech veneer</td><td>100</td><td>163-844</td><td>**</td><td>163-844</td><td>***</td><td>identical</td><td>non-porous, moderate</td></tr><tr><td>Golden oak veneer</td><td>250</td><td>163-844</td><td>o/*</td><td>163-844</td><td>o/*</td><td>worse</td><td>non-porous, moderate</td></tr></tbody></table>
On cooperative substrates (white furniture surface, glass and 5 Euro cent coin) the UV-VIS spectroscopy images yield satisfactory visibility throughout the entire spectrum with *S* exceeding 0.5. On copy paper the images are equally unusable as those captured using a CWL sensor. On beech veneer the visibility is identical with that on CWL images. On non-metallic paint, brushed stainless steel and golden oak veneer the visibility is slightly worse than that of a CWL sensor. The calculation of *S* on structured surfaces (beech veneer, golden oak veneer, and brushed stainless steel) makes no sense since the streakiness of a surface dominates that of a fingerprint. Most notably, the spectrometer acquires a perfect fingerprint image within the UV band on metallic paint, whereas the transparent layer causes non-deterministic noise while using a CWL sensor [6]. Here, *S* yields 0.7 between 200 and 400nm and 0.05 for the spectrum of visible light. This can be explained by the fact that the covering transparent paint is non-transparent for UV radiation. On blued metal, fresh fingerprints are slightly visible in the UV band, which is an improvement compared to a CWL sensor.

The important conclusion is that the streakiness score generally reflects judgments of human experts about the fingerprint visibility. However, in order to extract meaningful streakiness scores a substrate specific preprocessing is necessary.

## Acknowledgement

The authors wish to thank Michael Ulrich for the list of common substrates from crime scenes. The work in this paper has been funded in part by the German Federal Ministry of Education and Science (BMBF) through the Research Program under Contract No. FKZ: 13N10818.

## References

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4. S. M. Bleay, V. G. Sears et al.: Fingerprint Source Book, Home Office, 2012, https://www.gov.uk/government/publications/fingerprint-source-book

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