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Using headlight patterns in vehicle recognition
Forensic Science International 307 (2020) 110120
Contents lists available at ScienceDirect
Forensic Science International journal homepage: www.elsevier.com/locate/forsciint
Technical Note
Using headlight patterns in vehicle recognition Nir Finkelstein* , Alan Chaikovsky, Yaron Cohen, Tsadok Tsach Toolmarks and Materials Laboratory, Division of Identification and Forensic Science, Israel Police Headquarters, Jerusalem, Israel
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 January 2019 Received in revised form 13 November 2019 Accepted 12 December 2019 Available online 19 December 2019
Crime scenes are frequently poorly lit, making it difficult to recognize and identify implicated vehicles that have been caught on film or photographed during incidents. This paper explores vehicle recognition capabilities in dark images, specifically as when a vehicle’s headlights are on and are projecting light onto a flat vertical surface. In this study, the headlight reflection patterns of 68 vehicles were photographed and analyzed. This paper presents a method for confirming or ruling out a vehicle's make and model by its headlight pattern. © 2019 Elsevier B.V. All rights reserved.
Keywords: Forensic science Criminalistics Vehicle Headlights Headlight pattern Vehicle identification Vehicle headlight pattern
1. Introduction The last decade has seen a major increase in numbers of surveillance and outdoor cameras deployed in our surroundings as well as growing use of road cameras, dashboard cameras and phone cameras. Combined with the constant improvement in camera quality and the decline in their cost, the amount of photographic and video evidence from criminal acts recorded on camera is on the rise. Consequently, forensic scientists are impelled to address the problem of identifying vehicles in video footage and photographs. Forensic experts regularly use image enhancement technology and software models to identify vehicles by shape [1– 4]. But, in dark conditions, it is much harder to identify a vehicle by its shape than in a well-lit environment (Fig. 1). When an image contains the vehicle and a light source (as when the headlights are on), the large difference in brightness between the light source and its surroundings makes the subject of the image look darker [1], therefor difficult to identify This study, proposes a new method for analyzing photographs of cars taken in dark scenes, which also contain an image of light projected from a vehicle’s headlights. Such images of projected light occur when there is a flat vertical surface located in front of the headlights. Examples for such cases occur when a suspected vehicle is photographed in a dark crime scene in front of a parking gate, wall, fence, barrier, or another vehicle standing at right angles to the vehicle of interest.
* Corresponding author. E-mail address: [email protected] (N. Finkelstein). http://dx.doi.org/10.1016/j.forsciint.2019.110120 0379-0738/© 2019 Elsevier B.V. All rights reserved.
Headlight pattern is the conjunction of shapes of different levels of brightness that is created by the light projected from headlights and reflected off a flat vertical surface. This study tested whether headlight pattern is a characteristic pattern that is unique to car make and model-code, and in other words, whether it is possible to confirm or rule out specific car makes and models based on the reflected headlight pattern seen in photographs or captured stills. A search of the literature established that there are no publications regarding the examination of the headlight pattern in video footage and photographs. This study tested whether light projected from headlights and reflected off a flat vertical surface creates a pattern that is characteristic to car make and model. In other words, whether it is possible to confirm or rule out specific car makes and models based on the reflected headlight pattern seen in photographs or captured stills. The function of headlights is to illuminate the road lying in front and to the side of the vehicle under dark or low visibility conditions. The low-beam (dipped) lights provide light over a shorter distance than the high-beam lights [5]. Headlight design is an integral part of car design, and vehicle engineers create it early in the car design process. However, although specifications may differ, basic headlight structure comprises a housing; a bulb, as source of diffuse light; a bulb reflector, positioned in front of the bulb, to focus the light from the bulb into a reflector located behind the bulb (Figs. 2 and 3). When a driver turns on the headlights to illuminate the road ahead, light emitted from the bulb strikes the bulb-reflector, which concentrates the light and directs the beam to a reflector located behind the bulb. The reflector directs the light beam through a lens
2
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
Fig. 1. Light projected from vehicle headlights in a dark environment.
Fig. 4. Headlight optics.
Fig. 2. Typical vehicle headlight unit.
Fig. 5. Site of the experiment.
Fig. 3. Parts of a headlight unit.
pattern is largely affected by the design characteristics of the headlight's components: the housing, headlight bulb, bulb reflector, reflector, and lens [Figs. 2 and 3]. Vehicle manufacturers identify their car models with a unique identifier called a model code. The model code is distinct from the model name (the commercial brand name). When revisions are made to a specific model, the revised model is given a new unique identifier (a new model code) whether or not the model name has been modified. As a result, vehicles with the same model name may have different model codes. In this study, references to changes in model code, refer to revisions that affected headlight unit specifications. The goal of this study, is to determine whether the light pattern formed when projecting a headlight beam onto a flat vertical surface of a given car make, model name or model code is class characteristic. 2. Materials and methods
at the front of the headlight unit (Fig. 4) and the lens disperses the beam at an angle, intensity, and distance compliant with the international regulatory requirements [5] for low-beam lights (dipped) and high-beam lights. Both high and low-beam (dipped) lights will create a pattern when projected onto a vertical or a horizontal surface. Beam
In this study, the headlight patterns of 68 vehicles were photographed under the same calibrated, dark conditions and analyzed to establish identifying characteristics unique to the models. These 68 vehicles were divided as follows: For twelve car models, five similar vehicles of each model were analyzed and for
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
3
Fig. 6. Headlight patterns from five Hyundai i25 models with the same model code (CT41D). Left: low-beam, right: high-beam.
the other eight car models, one vehicle of each model was analyzed. To maintain consistency and allow comparison, the headlight patterns were photographed inside a hangar in the semi-dark. Vehicles were parked perpendicular to the projection surface at
two fixed horizontal distances from the projection surface (65 cm and 100 cm). Both high-beam and low-beam patterns were photographed at each of the distances. The photographer was placed at a distance from the projection surface, adjacent to the Bpillar, aiming to the headlights pattern. Fig. 5 shows the
4
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
Fig. 7. Headlight patterns from five Toyota Corollas of different model years and with different model codes. Left: low-beam, right: high-beam.
experiment site. The side length of the squares in the horizontal and vertical scales in the photographs is 10 cm. 3. Results and discussion Headlight patterns were photographed and all photographs were analyzed. Figs. 6–9 and Table 1 show representative findings
of the analysis. Fig. 6 shows headlight patterns obtained from cars of the same manufacturer, model name, and model code but of different model years (two 2016 vehicles and three 2017 vehicles). The analysis shows that vehicles of the exact same model name and model code, even if they were from different model years, had identical headlight patterns (Fig. 6). Vehicles with the same model name but a different model code, had a different headlight pattern.
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
5
Fig. 8. Comparing headlight patterns from different manufacturers. Group 1 (items 1–5 in Table 1). Left: low-beam, right: high-beam.
For example, Fig. 7 shows that although the 2013 Toyota Corolla has the same model name as the 2015 and 2017 models it has a different model code, and its headlight pattern is different. In addition, although the 2015 and 2017 models are two years apart, they have the same model code and so they produce an identical
headlight pattern. Conversely, the 2013 Corolla, again two model years away from the 2015 model, has a different model code and produces a different headlight pattern. Model codes reflect versions of car models. Because the headlights are part of the overall design of a vehicle, when the
6
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
Fig. 9. Comparing headlight patterns from different manufacturers. Group 2 (items 6–10 in Table 1). Left: low-beam, right: high-beam.
design of a vehicle's front is modified, the headlight design is usually modified. The resulting changes in headlight components and appearance, produce a headlight pattern that is characteristic of the model code.
Examination of different car makes with identical model names but different model codes, revealed the same result: every model code that signified a change in headlight unit specifications) had its own characteristic headlight pattern (Figs. 8 and 9) regardless of
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
7
Fig. 10. Headlight patterns from a Hyundai i25 projected from several distances. Left: low-beam, right: high-beam.
whether it was the low-beam lights or the high-beam lights. In addition, no difference was found in headlight pattern between rain-spattered headlights and the same headlights after they were dried with a towel. Based on these experiments, it was concluded that headlight pattern is a class characteristic determined by model code. Accordingly, it is possible to confirm, in a level of certainty that
allows further investigation, the model code (manufacturer, model name, model code) of a vehicle based on its headlight pattern. In cases where the forensic examiner has access to a headlight pattern of a suspect vehicle projected onto a flat vertical surface, the identity of the car can be confirmed or ruled out at model level using a database of patterns. A portion of such a data is shown in Figs. 6–9.
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N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
Table 1 Make and model information for the vehicles in Figs. 8 and 9. #
Manufacturer
Model code
Model Name
Year
Manufacturing country
1 (Fig. 8) 2 (Fig. 8) 3 (Fig. 8) 4 (Fig. 8) 5 (Fig. 8) 6 (Fig. 9) 7 (Fig. 9) 8 (Fig. 9) 9 (Fig. 9) 10 (Fig. 9)
SKODA PEUGEOT CITROEN CITROEN KIA TOYOTA TOYOTA TOYOTA KIA KIA
Octavia 508 C4 Picasso Jumpy Carnival Yaris Auris Avensis Rio Ceed
5E33MD 8D5FVA 3A5GZT/1S XTRHHA MC5C1 NHP130L-CHXNBW ZWE186L-DWXNBW ZRT272L-AEXEPW DN412B HN816G
2017 2014 2017 2015 2017 2016 2016 2014 2015 2017
Czech Republic France Spain France Korea France England England Korea Slovakia
Fig. 11. A crime scene as captured from surveillance camera.
A test examined how headlight pattern is affected by the distance between a vehicle’s headlights and the reflective surface (Fig. 10). Note that the pattern and its borders grow visibly less defined with distance, to the extent that at some point pattern identification is not possible. For example, when the headlight pattern of the Hyundai i25 was analyzed at distances of 100 400 cm the headlight pattern remained sufficiently distinct up to a distance of 150 cm. The comparison can be applied as long as the vehicle's headlight pattern is sufficiently distinct. This method will be used in practice in two ways when there is a headlight pattern photograph: 1) The lights of a suspected vehicle will be tested and the headlight pattern displayed in the test will be compared to the pattern in the photograph to determine if the suspected vehicle is the same model-code of the subject vehicle, and 2) the pattern in the photograph can be compared to a database of patterns acquired from testing to determine the make and model-code of the vehicle in the photograph (i.e. the suspected vehicle is unavailable). The following case demonstrates one of various implementations of the headlight pattern comparison method. A forensic toolmarks examiner received a video of a crime scene that had been captured in a surveillance camera. In the video, the victim's vehicle was shown parking. Suddenly, another vehicle (i.e. the killer's vehicle) appeared, with its headlights on.
The killer's vehicle pulled over in parallel to the victim's vehicle. Then, the killer stepped out of his vehicle, approached the victim's vehicle, shot the driver, returned into his car and drove away. During that period, the killer's vehicle headlights were projected onto the wall in front of the vehicle. The location of the surveillance camera and the lightning of the image made it impossible to identify the killer's vehicle. However, a clear headlight pattern of the killer's vehicle could be observed on the wall. The forensic examiner captured a photograph of the vehicle's headlight pattern from the video. Shortly afterwards, four suspicious vehicles, that had left the scene at the same time of the murder, were seized and brought to the laboratory for examination. The forensic examiner was requested to determine whether one of the suspected vehicles could be the killer's vehicle from the crime scene. Their headlight patterns of the four vehicles were photographed using the method presented above. A comparison between the headlight patterns of the four vehicles and those of the vehicle from the video was made. The forensic examiner determined that the characteristics of the headlights pattern of one of the four suspected vehicles (Hyundai i25 lowbeam) matched the headlight pattern from the photograph that had been captured from the video. In Fig. 11, the crime scene, as captured from the surveillance camera, is shown. In Fig. 12, a magnification of the vehicle's headlight pattern from the video (as seen in Fig. 11) and the headlight pattern photographs of the suspected vehicles are shown.
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Fig. 12. Comparison of headlight patterns from the crime scene to headlight patterns of the four suspected vehicles. Left: low-beam, right: high-beam.
4. Conclusions This study shows that the projected light pattern from a vehicle's headlights is a class characteristic of the car make, model name, and model code and it is not associated with the vehicle's year of manufacture. This means that a vehicle's model code is a definitive identifier of vehicle and headlight design, which in turn determines the projection pattern of the headlight beam.
When a forensic examiner is asked to identify a vehicle in a dark crime scene photo based on the vehicle's headlight pattern, a comparison can be made between the headlight pattern seen in the photograph and a reconstructed projection pattern from a suspect vehicle. This comparison can be used to eliminate or determine if the suspected vehicle is the same make and model-code of the subject vehicle in an investigation. If no suspect vehicle is available, the forensic expert can hypothesize what possible car make and
10
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
model codes would be suspect, or conversely, eliminate certain models from a suspect list. Future studies will examine the effects of different parameters such as: camera distance, angle of incidence of the camera on the surface, and camera height. Additionally, studies involving more vehicles will be presented. CRediT authorship contribution statement Nir Finkelstein: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Alan Chaikovsky: Investigation, Visualization. Yaron Cohen: Validation, Formal analysis, Writing - review & editing. Tsadok Tsach: Validation, Formal analysis, Writing review & editing.
References [1] R. Taktak, M. Dufaut, R. Husson, Vehicle detection at night using image processing and pattern recognition November, Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference, 2(1994) , pp. 296–300. [2] H. Chen, Y. Cheng, C. ZHANG, Fine-grained vehicle type recognition based on deep convolution neural networks, J. Hebei Univ. Sci. Technol. 38 (6) (2017) 564–569. [3] H. Fei, Front Vehicle Recognition Based on Automotive Vision Rev, Téc. Ing. Univ. Zulia, 2016. [4] M.E. Irhebhude, P.O. Odion, D.T. Chinyio, Centrog Feature technique for vehicle type recognition at day and night times, arXiv preprint arXiv 1612 (00645) (2016). [5] ECE (Economic Commission for Europe), Regulation No 48 of the Economic Commission for Europe of the United Nations (UNECE)—Uniform Provisions Concerning the Approval of Vehicles With Regard to the Installation of Lighting and Light-Signaling Devices [2016/1723], (2016) . https://publications.europa. eu/en/publication-detail/-/publication/192086c4-870f-11e6-b07601aa75ed71a1/language-en.
Contents lists available at ScienceDirect
Forensic Science International journal homepage: www.elsevier.com/locate/forsciint
Technical Note
Using headlight patterns in vehicle recognition Nir Finkelstein* , Alan Chaikovsky, Yaron Cohen, Tsadok Tsach Toolmarks and Materials Laboratory, Division of Identification and Forensic Science, Israel Police Headquarters, Jerusalem, Israel
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 January 2019 Received in revised form 13 November 2019 Accepted 12 December 2019 Available online 19 December 2019
Crime scenes are frequently poorly lit, making it difficult to recognize and identify implicated vehicles that have been caught on film or photographed during incidents. This paper explores vehicle recognition capabilities in dark images, specifically as when a vehicle’s headlights are on and are projecting light onto a flat vertical surface. In this study, the headlight reflection patterns of 68 vehicles were photographed and analyzed. This paper presents a method for confirming or ruling out a vehicle's make and model by its headlight pattern. © 2019 Elsevier B.V. All rights reserved.
Keywords: Forensic science Criminalistics Vehicle Headlights Headlight pattern Vehicle identification Vehicle headlight pattern
1. Introduction The last decade has seen a major increase in numbers of surveillance and outdoor cameras deployed in our surroundings as well as growing use of road cameras, dashboard cameras and phone cameras. Combined with the constant improvement in camera quality and the decline in their cost, the amount of photographic and video evidence from criminal acts recorded on camera is on the rise. Consequently, forensic scientists are impelled to address the problem of identifying vehicles in video footage and photographs. Forensic experts regularly use image enhancement technology and software models to identify vehicles by shape [1– 4]. But, in dark conditions, it is much harder to identify a vehicle by its shape than in a well-lit environment (Fig. 1). When an image contains the vehicle and a light source (as when the headlights are on), the large difference in brightness between the light source and its surroundings makes the subject of the image look darker [1], therefor difficult to identify This study, proposes a new method for analyzing photographs of cars taken in dark scenes, which also contain an image of light projected from a vehicle’s headlights. Such images of projected light occur when there is a flat vertical surface located in front of the headlights. Examples for such cases occur when a suspected vehicle is photographed in a dark crime scene in front of a parking gate, wall, fence, barrier, or another vehicle standing at right angles to the vehicle of interest.
* Corresponding author. E-mail address: [email protected] (N. Finkelstein). http://dx.doi.org/10.1016/j.forsciint.2019.110120 0379-0738/© 2019 Elsevier B.V. All rights reserved.
Headlight pattern is the conjunction of shapes of different levels of brightness that is created by the light projected from headlights and reflected off a flat vertical surface. This study tested whether headlight pattern is a characteristic pattern that is unique to car make and model-code, and in other words, whether it is possible to confirm or rule out specific car makes and models based on the reflected headlight pattern seen in photographs or captured stills. A search of the literature established that there are no publications regarding the examination of the headlight pattern in video footage and photographs. This study tested whether light projected from headlights and reflected off a flat vertical surface creates a pattern that is characteristic to car make and model. In other words, whether it is possible to confirm or rule out specific car makes and models based on the reflected headlight pattern seen in photographs or captured stills. The function of headlights is to illuminate the road lying in front and to the side of the vehicle under dark or low visibility conditions. The low-beam (dipped) lights provide light over a shorter distance than the high-beam lights [5]. Headlight design is an integral part of car design, and vehicle engineers create it early in the car design process. However, although specifications may differ, basic headlight structure comprises a housing; a bulb, as source of diffuse light; a bulb reflector, positioned in front of the bulb, to focus the light from the bulb into a reflector located behind the bulb (Figs. 2 and 3). When a driver turns on the headlights to illuminate the road ahead, light emitted from the bulb strikes the bulb-reflector, which concentrates the light and directs the beam to a reflector located behind the bulb. The reflector directs the light beam through a lens
2
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
Fig. 1. Light projected from vehicle headlights in a dark environment.
Fig. 4. Headlight optics.
Fig. 2. Typical vehicle headlight unit.
Fig. 5. Site of the experiment.
Fig. 3. Parts of a headlight unit.
pattern is largely affected by the design characteristics of the headlight's components: the housing, headlight bulb, bulb reflector, reflector, and lens [Figs. 2 and 3]. Vehicle manufacturers identify their car models with a unique identifier called a model code. The model code is distinct from the model name (the commercial brand name). When revisions are made to a specific model, the revised model is given a new unique identifier (a new model code) whether or not the model name has been modified. As a result, vehicles with the same model name may have different model codes. In this study, references to changes in model code, refer to revisions that affected headlight unit specifications. The goal of this study, is to determine whether the light pattern formed when projecting a headlight beam onto a flat vertical surface of a given car make, model name or model code is class characteristic. 2. Materials and methods
at the front of the headlight unit (Fig. 4) and the lens disperses the beam at an angle, intensity, and distance compliant with the international regulatory requirements [5] for low-beam lights (dipped) and high-beam lights. Both high and low-beam (dipped) lights will create a pattern when projected onto a vertical or a horizontal surface. Beam
In this study, the headlight patterns of 68 vehicles were photographed under the same calibrated, dark conditions and analyzed to establish identifying characteristics unique to the models. These 68 vehicles were divided as follows: For twelve car models, five similar vehicles of each model were analyzed and for
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
3
Fig. 6. Headlight patterns from five Hyundai i25 models with the same model code (CT41D). Left: low-beam, right: high-beam.
the other eight car models, one vehicle of each model was analyzed. To maintain consistency and allow comparison, the headlight patterns were photographed inside a hangar in the semi-dark. Vehicles were parked perpendicular to the projection surface at
two fixed horizontal distances from the projection surface (65 cm and 100 cm). Both high-beam and low-beam patterns were photographed at each of the distances. The photographer was placed at a distance from the projection surface, adjacent to the Bpillar, aiming to the headlights pattern. Fig. 5 shows the
4
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
Fig. 7. Headlight patterns from five Toyota Corollas of different model years and with different model codes. Left: low-beam, right: high-beam.
experiment site. The side length of the squares in the horizontal and vertical scales in the photographs is 10 cm. 3. Results and discussion Headlight patterns were photographed and all photographs were analyzed. Figs. 6–9 and Table 1 show representative findings
of the analysis. Fig. 6 shows headlight patterns obtained from cars of the same manufacturer, model name, and model code but of different model years (two 2016 vehicles and three 2017 vehicles). The analysis shows that vehicles of the exact same model name and model code, even if they were from different model years, had identical headlight patterns (Fig. 6). Vehicles with the same model name but a different model code, had a different headlight pattern.
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
5
Fig. 8. Comparing headlight patterns from different manufacturers. Group 1 (items 1–5 in Table 1). Left: low-beam, right: high-beam.
For example, Fig. 7 shows that although the 2013 Toyota Corolla has the same model name as the 2015 and 2017 models it has a different model code, and its headlight pattern is different. In addition, although the 2015 and 2017 models are two years apart, they have the same model code and so they produce an identical
headlight pattern. Conversely, the 2013 Corolla, again two model years away from the 2015 model, has a different model code and produces a different headlight pattern. Model codes reflect versions of car models. Because the headlights are part of the overall design of a vehicle, when the
6
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
Fig. 9. Comparing headlight patterns from different manufacturers. Group 2 (items 6–10 in Table 1). Left: low-beam, right: high-beam.
design of a vehicle's front is modified, the headlight design is usually modified. The resulting changes in headlight components and appearance, produce a headlight pattern that is characteristic of the model code.
Examination of different car makes with identical model names but different model codes, revealed the same result: every model code that signified a change in headlight unit specifications) had its own characteristic headlight pattern (Figs. 8 and 9) regardless of
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
7
Fig. 10. Headlight patterns from a Hyundai i25 projected from several distances. Left: low-beam, right: high-beam.
whether it was the low-beam lights or the high-beam lights. In addition, no difference was found in headlight pattern between rain-spattered headlights and the same headlights after they were dried with a towel. Based on these experiments, it was concluded that headlight pattern is a class characteristic determined by model code. Accordingly, it is possible to confirm, in a level of certainty that
allows further investigation, the model code (manufacturer, model name, model code) of a vehicle based on its headlight pattern. In cases where the forensic examiner has access to a headlight pattern of a suspect vehicle projected onto a flat vertical surface, the identity of the car can be confirmed or ruled out at model level using a database of patterns. A portion of such a data is shown in Figs. 6–9.
8
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
Table 1 Make and model information for the vehicles in Figs. 8 and 9. #
Manufacturer
Model code
Model Name
Year
Manufacturing country
1 (Fig. 8) 2 (Fig. 8) 3 (Fig. 8) 4 (Fig. 8) 5 (Fig. 8) 6 (Fig. 9) 7 (Fig. 9) 8 (Fig. 9) 9 (Fig. 9) 10 (Fig. 9)
SKODA PEUGEOT CITROEN CITROEN KIA TOYOTA TOYOTA TOYOTA KIA KIA
Octavia 508 C4 Picasso Jumpy Carnival Yaris Auris Avensis Rio Ceed
5E33MD 8D5FVA 3A5GZT/1S XTRHHA MC5C1 NHP130L-CHXNBW ZWE186L-DWXNBW ZRT272L-AEXEPW DN412B HN816G
2017 2014 2017 2015 2017 2016 2016 2014 2015 2017
Czech Republic France Spain France Korea France England England Korea Slovakia
Fig. 11. A crime scene as captured from surveillance camera.
A test examined how headlight pattern is affected by the distance between a vehicle’s headlights and the reflective surface (Fig. 10). Note that the pattern and its borders grow visibly less defined with distance, to the extent that at some point pattern identification is not possible. For example, when the headlight pattern of the Hyundai i25 was analyzed at distances of 100 400 cm the headlight pattern remained sufficiently distinct up to a distance of 150 cm. The comparison can be applied as long as the vehicle's headlight pattern is sufficiently distinct. This method will be used in practice in two ways when there is a headlight pattern photograph: 1) The lights of a suspected vehicle will be tested and the headlight pattern displayed in the test will be compared to the pattern in the photograph to determine if the suspected vehicle is the same model-code of the subject vehicle, and 2) the pattern in the photograph can be compared to a database of patterns acquired from testing to determine the make and model-code of the vehicle in the photograph (i.e. the suspected vehicle is unavailable). The following case demonstrates one of various implementations of the headlight pattern comparison method. A forensic toolmarks examiner received a video of a crime scene that had been captured in a surveillance camera. In the video, the victim's vehicle was shown parking. Suddenly, another vehicle (i.e. the killer's vehicle) appeared, with its headlights on.
The killer's vehicle pulled over in parallel to the victim's vehicle. Then, the killer stepped out of his vehicle, approached the victim's vehicle, shot the driver, returned into his car and drove away. During that period, the killer's vehicle headlights were projected onto the wall in front of the vehicle. The location of the surveillance camera and the lightning of the image made it impossible to identify the killer's vehicle. However, a clear headlight pattern of the killer's vehicle could be observed on the wall. The forensic examiner captured a photograph of the vehicle's headlight pattern from the video. Shortly afterwards, four suspicious vehicles, that had left the scene at the same time of the murder, were seized and brought to the laboratory for examination. The forensic examiner was requested to determine whether one of the suspected vehicles could be the killer's vehicle from the crime scene. Their headlight patterns of the four vehicles were photographed using the method presented above. A comparison between the headlight patterns of the four vehicles and those of the vehicle from the video was made. The forensic examiner determined that the characteristics of the headlights pattern of one of the four suspected vehicles (Hyundai i25 lowbeam) matched the headlight pattern from the photograph that had been captured from the video. In Fig. 11, the crime scene, as captured from the surveillance camera, is shown. In Fig. 12, a magnification of the vehicle's headlight pattern from the video (as seen in Fig. 11) and the headlight pattern photographs of the suspected vehicles are shown.
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
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Fig. 12. Comparison of headlight patterns from the crime scene to headlight patterns of the four suspected vehicles. Left: low-beam, right: high-beam.
4. Conclusions This study shows that the projected light pattern from a vehicle's headlights is a class characteristic of the car make, model name, and model code and it is not associated with the vehicle's year of manufacture. This means that a vehicle's model code is a definitive identifier of vehicle and headlight design, which in turn determines the projection pattern of the headlight beam.
When a forensic examiner is asked to identify a vehicle in a dark crime scene photo based on the vehicle's headlight pattern, a comparison can be made between the headlight pattern seen in the photograph and a reconstructed projection pattern from a suspect vehicle. This comparison can be used to eliminate or determine if the suspected vehicle is the same make and model-code of the subject vehicle in an investigation. If no suspect vehicle is available, the forensic expert can hypothesize what possible car make and
10
N. Finkelstein et al. / Forensic Science International 307 (2020) 110120
model codes would be suspect, or conversely, eliminate certain models from a suspect list. Future studies will examine the effects of different parameters such as: camera distance, angle of incidence of the camera on the surface, and camera height. Additionally, studies involving more vehicles will be presented. CRediT authorship contribution statement Nir Finkelstein: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Writing - original draft, Writing - review & editing, Visualization, Supervision, Project administration. Alan Chaikovsky: Investigation, Visualization. Yaron Cohen: Validation, Formal analysis, Writing - review & editing. Tsadok Tsach: Validation, Formal analysis, Writing review & editing.
References [1] R. Taktak, M. Dufaut, R. Husson, Vehicle detection at night using image processing and pattern recognition November, Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference, 2(1994) , pp. 296–300. [2] H. Chen, Y. Cheng, C. ZHANG, Fine-grained vehicle type recognition based on deep convolution neural networks, J. Hebei Univ. Sci. Technol. 38 (6) (2017) 564–569. [3] H. Fei, Front Vehicle Recognition Based on Automotive Vision Rev, Téc. Ing. Univ. Zulia, 2016. [4] M.E. Irhebhude, P.O. Odion, D.T. Chinyio, Centrog Feature technique for vehicle type recognition at day and night times, arXiv preprint arXiv 1612 (00645) (2016). [5] ECE (Economic Commission for Europe), Regulation No 48 of the Economic Commission for Europe of the United Nations (UNECE)—Uniform Provisions Concerning the Approval of Vehicles With Regard to the Installation of Lighting and Light-Signaling Devices [2016/1723], (2016) . https://publications.europa. eu/en/publication-detail/-/publication/192086c4-870f-11e6-b07601aa75ed71a1/language-en.