IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i3d10.1007_s40745-022-00408-6.html
   My bibliography  Save this article

A Comprehensive Review on Computer Vision and Fuzzy Logic in Forensic Science Application

Author

Listed:
  • Prarthi Thakkar

    (Indus University)

  • Darshil Patel

    (Indus University)

  • Isha Hirpara

    (Indus University)

  • Jinesh Jagani

    (Indus University)

  • Smit Patel

    (Indus University)

  • Manan Shah

    (Pandit Deendayal Energy University)

  • Ameya Kshirsagar

    (Symbiosis Institute of Technology)

Abstract

Criminalistics is another name for forensic science. It uses science in a criminal investigation as governed by judicial criteria of acceptable, relevant, and admissible evidence and criminal procedure. Forensic science has been around for a long time and has seen considerable changes, from fingerprint identification to DNA analysis and digital forensics. The study focuses on the most critical technologies in forensic science, then deconstructs numerous computer vision, image processing, and fuzzy logic methodologies in the large subject of forensic research. It also addresses the prospects for using the technology in approaches ranging from biometric identification to a 3D reconstruction of a crime scene. To some extent, adopting the numerous methodologies outlined in the paper helps overcome the disadvantages and challenges of traditional forensics procedures. Furthermore, some constraints are taken into account. For example, in various ways, the primary evidence is pre-processed and translated to an intermediate or more lucid form before the crux algorithms are applied. As a result, there is still plenty of room for research in this subject, such as developing solid algorithms, making the technology accept raw data, etc. If utilized correctly, forensic science technology has the potential to affect a paradigm shift in the criminal justice system.

Suggested Citation

  • Prarthi Thakkar & Darshil Patel & Isha Hirpara & Jinesh Jagani & Smit Patel & Manan Shah & Ameya Kshirsagar, 2023. "A Comprehensive Review on Computer Vision and Fuzzy Logic in Forensic Science Application," Annals of Data Science, Springer, vol. 10(3), pages 761-785, June.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:3:d:10.1007_s40745-022-00408-6
    DOI: 10.1007/s40745-022-00408-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-022-00408-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-022-00408-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rameswari Poornima Janardanan & Rajasvaran Logeswaran, 2018. "Recent Image Processing Techniques in Forensic Odontology - A Systematic Review," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 2(5), pages 1-6, March.
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heba Soltan Mohamed & M. Masoom Ali & Haitham M. Yousof, 2023. "The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance," Annals of Data Science, Springer, vol. 10(5), pages 1199-1216, October.
    2. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    3. Mansoureh Beheshti Nejad & Seyed Mahmoud Zanjirchi & Seyed Mojtaba Hosseini Bamakan & Negar Jalilian, 2024. "Blockchain Adoption in Operations Management: A Systematic Literature Review of 14 Years of Research," Annals of Data Science, Springer, vol. 11(4), pages 1361-1389, August.
    4. M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.
    5. Amaal Elsayed Mubarak & Ehab Mohamed Almetwally, 2024. "Modelling and Forecasting of Covid-19 Using Periodical ARIMA Models," Annals of Data Science, Springer, vol. 11(4), pages 1483-1502, August.
    6. Xueyan Xu & Fusheng Yu & Runjun Wan, 2023. "A Determining Degree-Based Method for Classification Problems with Interval-Valued Attributes," Annals of Data Science, Springer, vol. 10(2), pages 393-413, April.
    7. Qinghua Zheng & Chutong Yang & Haijun Yang & Jianhe Zhou, 2020. "A Fast Exact Algorithm for Deployment of Sensor Nodes for Internet of Things," Information Systems Frontiers, Springer, vol. 22(4), pages 829-842, August.
    8. Prashant Singh & Prashant Verma & Nikhil Singh, 2022. "Offline Signature Verification: An Application of GLCM Features in Machine Learning," Annals of Data Science, Springer, vol. 9(6), pages 1309-1321, December.
    9. Shah Hussain & Muhammad Qasim Khan, 2023. "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, Springer, vol. 10(3), pages 637-655, June.
    10. A. R. Sherwani & Q. M. Ali, 2023. "Parametric Classification using Fuzzy Approach for Handling the Problem of Mixed Pixels in Ground Truth Data for a Satellite Image," Annals of Data Science, Springer, vol. 10(6), pages 1459-1472, December.
    11. Hui Zheng & Peng LI & Jing HE, 2022. "A Novel Association Rule Mining Method for Streaming Temporal Data," Annals of Data Science, Springer, vol. 9(4), pages 863-883, August.
    12. Rakhal Das & Anjan Mukherjee & Binod Chandra Tripathy, 2022. "Application of Neutrosophic Similarity Measures in Covid-19," Annals of Data Science, Springer, vol. 9(1), pages 55-70, February.
    13. Sankalp Loomba & Madhavi Dave & Harshal Arolkar & Sachin Sharma, 2024. "Sentiment Analysis using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency," Annals of Data Science, Springer, vol. 11(6), pages 2019-2034, December.
    14. Desmond Chekwube Bartholomew & Chrysogonus Chinagorom Nwaigwe & Ukamaka Cynthia Orumie & Godwin Onyeka Nwafor, 2024. "Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series," Annals of Data Science, Springer, vol. 11(5), pages 1609-1634, October.
    15. Mahabuba Akhter & Syed Md. Minhaz Hossain & Rizma Sijana Nigar & Srabanti Paul & Khaleque Md. Aashiq Kamal & Anik Sen & Iqbal H. Sarker, 2024. "COVID-19 Fake News Detection using Deep Learning Model," Annals of Data Science, Springer, vol. 11(6), pages 2167-2198, December.
    16. Muhammad Tahir & Sufyan Ali & Ayesha Sohail & Ying Zhang & Xiaohua Jin, 2024. "Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects," Annals of Data Science, Springer, vol. 11(4), pages 1421-1434, August.
    17. Junjie Hou & Yuqi Zhang & Duo Su, 2024. "Unified Image Harmonization with Region Augmented Attention Normalization," Annals of Data Science, Springer, vol. 11(5), pages 1865-1886, October.
    18. Muhammed Navas Thorakkattle & Shazia Farhin & Athar Ali khan, 2022. "Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA," Annals of Data Science, Springer, vol. 9(5), pages 1025-1047, October.
    19. Bo Li & Guangle Du, 2024. "Reaction Function for Financial Market Reacting to Events or Information," Annals of Data Science, Springer, vol. 11(4), pages 1265-1290, August.
    20. Siying Guo & Jianxuan Liu & Qiu Wang, 2022. "Effective Learning During COVID-19: Multilevel Covariates Matching and Propensity Score Matching," Annals of Data Science, Springer, vol. 9(5), pages 967-982, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:10:y:2023:i:3:d:10.1007_s40745-022-00408-6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.