Unraveling the Unseen:
In our Forensic Mysteries studies, we explore how our Synthesize Vision™ framework supports forensic teams by enhancing the visual interpretation of evidence and witness descriptions. This section showcases our collaborative efforts to provide detailed and accurate visual composites that complement traditional forensic methods.
.case studies
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Forensic Mysteries
Enhancing Composite Drawings
001
Federal Bureau of Investigation [FBI]
Police Department
Challenge
Suspect identification based on eyewitness testimony is crucial in criminal investigations.
Traditional facial composite sketches often lack the precision needed for clear identification.
Our challenge was to transform these composite sketches into photorealistic renders to enhance their utility in law enforcement.
Approach
We consulted with law enforcement agencies to understand their needs and the limitations of traditional composite sketches.
Our research focused on AI and machine learning models capable of converting sketches into photorealistic images.
We collected a comprehensive dataset of composite sketches and corresponding photographs to train our AI model.
Solution
We deployed advanced AI algorithms to interpret and enhance composite sketches into realistic images
and are currently developing a user-friendly interface for forensic artists to easily obtain high-quality photorealistic outputs.
D. B. Cooper, also known as Dan Cooper, was an unidentified man who hijacked Northwest Orient Airlines Flight 305, a Boeing 727 aircraft, in United States airspace on November 24, 1971.
Orange County
Sheriff's Office Composite Drawings
Sheriff's Office Composite Drawings
Forensic Mysteries
generative renders from post-mortem visual assets
002
Federal Bureau of Investigation [FBI]
Police Department
A postmortem drawing is one that is generated when human remains are found in reasonably good condition. The forensic artist works from morgue photographs, crime scene photographs or by viewing the actual body. The forensic artist is asked to create an approximate facial likeness in order to help provide an identity to an unidentified decedent.
the accurate reconstruction of post-mortem visuals plays a crucial role in investigations and identification processes. Traditional methods often rely on artist renditions and limited data, resulting in less precise reconstructions. The challenge was to leverage AI and machine learning to transform post-mortem visual assets into highly detailed and lifelike images, enhancing the accuracy and reliability of forensic reconstructions.
collecting extensive datasets of post-mortem images, photos, and corresponding lifelike images. This data was meticulously analyzed to understand the intricacies and patterns necessary for accurate reconstruction.
Utilizing advanced machine learning algorithms, we developed a framework capable of interpreting and enhancing post-mortem visuals.
The AI-driven solution we developed converts post-mortem visual assets, such as sketches, photos or incomplete visual data, into photorealistic and lifelike images. This technology leverages sophisticated machine learning algorithms to generate highly accurate and detailed reconstructions, providing forensic experts with powerful tools for identification and investigation
Disclaimer:
This presentation contains photos of deceased individuals, which may be sensitive or distressing to some viewers. Viewer discretion is advised.
This presentation contains photos of deceased individuals, which may be sensitive or distressing to some viewers. Viewer discretion is advised.
Right Image: Photograph of the victim in life after being identified from the drawing.
Forensic Mysteries
time dependent variables
003
003
Federal Bureau of Investigation [FBI]
Police Department
future release
age + weight manipulation
In forensic investigations, accurately predicting how a person's appearance changes over time is crucial, particularly for locating wanted fugitives or missing children. Traditional methods involve forensic artists creating age progressions based on the last known photograph and additional information such as lifestyle, employment, and medical history. However, these manual techniques often lack precision due to the inherent complexity and variability of human aging. The challenge was to leverage machine learning and generative AI to analyze aging factors and produce highly accurate approximations of the aging process, enhancing the precision and reliability of forensic age progressions.
Approach
Using advanced machine learning algorithms, we developed a model capable of simulating time dependent variables such as age and weight progression. The model was trained on our dataset to recognize and predict changes in facial features, skin texture, and body morphology as influenced by various factors.
Solution
By harnessing the power of machine learning and generative AI, we developed an advanced tool for forensic age and weight manipulation. Detectives can now provide last known photograph, sketch and relevant background information, and our AI-driven solution generates highly accurate approximations of the individual's appearance over time.
This technology significantly enhances the precision and reliability of forensic age progressions, aiding law enforcement in locating wanted fugitives and missing children more effectively.