Nanomaterials have a positive impact on latent fingerprint revealing technologies for complex items due to their small particle size, large specific surface area, and surface activity. The purpose of this experiment is to look at how well VO2-based nanomaterials work as latent fingerprinting tools. The produced particles were described, demonstrating that their structure and properties are adequate for forensic science to develop fingerprints. Later, the dusting and brushing approach was used to analyze the fingerprint development effect on six various objects, including tiles and glass, and compare it to the conventional powder's traditional revealing effect. The results indicate the establishment of a sensitive novel fingerprint development method since the VO2 nanoparticles have great sensitivity, can disclose crisp papillary lines and detailed features of fingerprints, and are suited for most common carrier objects. The advantages of the created particles, however, include time savings, simplicity of use, and affordable price.
In order to improve the efficiency of bullet marks inspection and meet the requirements of timeliness and accuracy of bullet marks inspection, it is urgent to make use of the powerful performance of artificial intelligence to develop a deep learing-based gun rifle-comparing system that can achieve accurate identification, rapid inspection and scientific qualitative. To achieve rapid combination and rapid tracing, to provide strong clues and technical support for rapid case detection.
This paper introduces the development background of traditional bullet mark inspection, summarizes the existing automatic recognition technology of bullet mark inspection and its shortcomings, analyzes the application status of deep learning in bullet mark inspection, and finally prospects the application of deep learning in rifling comparison technology of bullet.
Bloodstains, as a biological test material with a high occurrence rate at the scene of a criminal case, can provide a great deal of clue to the case. How to quickly, accurately and non-destructively distinguish between bloodstains and suspected bloodstains in a crime scene and identify the species of bloodstains is not only necessary for the examination of physical evidence in judicial practice, but also an inevitable requirement for the public security authorities to identify the case and reveal the falsified scene. Using a hyperspectral imager, we acquired hyperspectral data from 26 experimental materials, including 13 animal bloodstains and 13 suspected bloodstain compounds. This paper focuses on the identification of bloodstain species. The experimental technique builds a classification model for the identification of bloodstain species using three algorithms: K-nearest neighbor, Support Vector Machine, and Naive Bayes classification. The experimental results showed that the Naive Bayes classification algorithm and the Support Vector Machine classification model with RBF kernel function and Sigmoid as kernel function were the most efficient. Experiment classification accuracyof100.00% was achieved in all cases. Our experiments explore a new method for identifying different species of bloodstains using hyperspectral imaging technology.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.