{ "ok": true, "doi": "10.46243/jst.2021.v6.i05.pp177-184", "doi_display": "10.46243/jst.2021.v6.i05.pp177-184", "doi_url": "https://doi.org/10.46243/jst.2021.v6.i05.pp177-184", "state": "registered", "url": "https://www.jst.org.in/index.php/pub/article/view/912", "title": "CRIMINAL DETECTION USING FACE RECOGNITION SYSTEM", "version": 2, "registered_via": "crossref", "prefix": { "prefix": "10.46243", "status": "live" }, "registrant": { "name": "Longman Publishers", "kind": "publisher", "country": "India" }, "reserved_at": null, "registered_at": "2026-09-29 22:00:41", "updated_at": "2026-09-30 00:00:01", "withdrawn_at": null, "withdrawn_reason": null, "record": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2021.v6.i05.pp177-184", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "CRIMINAL DETECTION USING FACE RECOGNITION SYSTEM", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2021.v6.i05.pp177-184" } ], "agents": [ { "role": "author", "name": { "given": "Dr.Syed", "family": "Abdul Sattar" }, "sequence": "first" }, { "role": "author", "name": { "given": "Mohd Khaleel", "family": "Ahmed" }, "sequence": "additional" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2021-09-05", "date_type": "PublicationDate", "online": "2021-09-05" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "06", "issue": "05", "pages": { "first": "177", "last": "184" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/912", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/912/838", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/912/2570", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://www.jst.org.in/index.php/pub/article/view/912/838", "purpose": "similarity-checking" } ], "abstract": { "value": "The face is crucial for human identity. It is the feature which best distinguishes a person. Face recognition is an interesting and challenging problem and impacts important applications in many areas such as identification for law enforcement, authentication for banking and security system access and personal identification among others. Face recognition is an easy task for humans but it’s an entirely different and difficult task for a computer. Face recognition based on the geometric features of a face is probably the most instinctive approach for human identification. The whole process can be divided into three major steps where the first step is to find a good database of faces with multiple images for each individual. The next step is to detect faces in the database images and use them to train the face recognizer and the final step is to test the face recognizer, if it recognizes the faces it was trained with. There is an abnormal increase in the crime rate and also the number of criminals. This leads towards a great concern about the security issues. Crime prevention and criminal identification are the primary issues that police personnel face. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The CCTV footage can be used to identify suspects on the scene. The model will be able to recognize criminals, whose pictures the model is initially trained with, using Convolution Neural Networks, Artificial Neural Networks and OpenCV and further send a message to the cops about the location and other details of the criminal.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2021-09-05", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/34.598228", "unstructured": "Belhumeur, P. N., Hespanha, J. P., &Kriegman, D. J. (1997). Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 19, pp. 711-720. IEEE Computer Society" }, { "key": "ref2", "doi": "10.1535/itj.0902.03", "unstructured": "Bornet, O. (2005, May 19). Learning Based Computer Vision with Intel's Open Source Computer Vision Library. Retrieved April 2007, 2007, from Intel.com Website: http://www.intel.com/technology/itj/2005/volume09issue02/art03_learning_vision/p04_face_ dete ction.html" }, { "key": "ref3", "doi": "10.1109/34.254061", "unstructured": "Brunelli, R., &Poggio, T. (1993). Face Recognition: Features versus templates. IEEE Transaction on Pattern Analysis and Machine Intelligence, 15 (10), 1042-1052" }, { "key": "ref4", "doi": "10.1109/cvpr.2001.990517", "unstructured": "Viola, P. and Jones, M. Rapid object detection using boosted cascade of simple features. IEEE Conference on Computer Vision and Pattern Recognition, 2001" }, { "key": "ref5", "doi": "10.1023/b:visi.0000013087.49260.fb", "unstructured": "P. Viola and M. Jones. Robust Real-time Object Detection. International Journal of Computer Vision, 57(2):137–154, 2002" }, { "key": "ref6", "doi": "10.3724/sp.j.1016.2009.00336", "unstructured": "Hui-Xing, J., Yu-Jin, Z.: Fast Adaboost Training Algorithm by Dynamic Weight Trimming. Chinese Journal of Computers (2009)" }, { "key": "ref7", "unstructured": "Bureau of Justice Statistics, U.S. Department of Justice, April 1990), pp. 43-66; SEARCH Group, “Legal and Policy Issues Relating to Biometric Identification Technologies”" }, { "key": "ref8", "doi": "10.1109/cvpr.1996.517076", "unstructured": "J. Cox, J. Ghosn, and P.N. Yianilos, “Feature-Based Face Recognition Using Mixture Distance,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1996, pp. 209-216" }, { "key": "ref9", "doi": "10.1145/971617.971618", "unstructured": "G.Wang, H. Chen, and H. Atabakhsh, “Automatically Detecting Deceptive Criminal Identities,” Comm. ACM, Mar. 2004, pp. 70-76" } ], "record": { "registrant": "Longman Publishers", "registered": "2026-09-12", "updated": "2026-09-20", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "record_sha256": "cd0d743dab72e44e73ebd37768195605fbd367229afd0474c61b073b385c68a3", "handle": { "synced_at": "2026-10-01 18:13:58", "url": "https://www.jst.org.in/index.php/pub/article/view/912" }, "links": { "record_page": "https://registry.smartscholars.in/record.php?doi=10.46243%2Fjst.2021.v6.i05.pp177-184", "system_metadata": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i05.pp177-184&as=system", "history": "https://registry.smartscholars.in/api.php?action=history&doi=10.46243%2Fjst.2021.v6.i05.pp177-184", "kernel_xml": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2021.v6.i05.pp177-184&as=xml" } }