ABSTRACT
The Philippines is a southeastern Asian archipelago of over 7,640 islands. The Philippine National Police (PNP) is tasked with upholding the law, preventing and controlling crime, maintaining peace and order, and ensuring public safety and internal security with the active support of the community. Currently, the country has 1,766 police stations. Criminal identification procedures take time to complete due to geographical challenges. To address such challenges, the Mobile Automated Fingerprint Identification System (MAFIS) was developed. The integration of face recognition with the existing MAFIS makes it MABIS, a Mobile Automated Biometric Identification System. The MABIS allows law enforcers to use both fingerprint and face recognition to identify law offenders by searching the criminal database for existing records. If found, criminal records will be retrieved for investigation and referenced. If no information is found, a new record will be added. The goal of the paper is to integrate a Face Recognition (FR) system into an existing MAFIS by employing an open-source facial recognition service.
- J. E. M. Angara, "An Act Establishing a National Crime Database," Republic of the Philippines Congress, Manila, Philippines, 2013.Google Scholar
- W. P. Rey & G. Rolluqui, "Mobile Automated Fingerprint Identification System (MAFIS): An Android-based Criminal Tracking System using Fingerprint Minutiae Structure," 2021 5th International Conference on E-Society, E-Education and E-Technology (ICSET 2021), Taipei, Taiwan, 2021. DOI: 10.1145/3485768.3485773Google ScholarDigital Library
- W.P. Rey & M.A. Baccay, “MAFIS Overlay Network: Towards a Secure Network for Mobile Automated Fingerprint Identification System over Virtual Private Network (VPN),” ICCBN 2022: 2022 10th International Conference on Communications and Broadband Networking (ICCBN). February 2022 Pages 20–27. https://doi.org/10.1145/3538806.3538812Google ScholarDigital Library
- W.P. Rey & R. Juanatas, “Towards a Performance Optimization of Mobile Automated Fingerprint Identification System (MAFIS) for the Philippine National Police”. ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial Intelligence, March 2022. Pages 380–386. https://doi.org/10.1145/3532213.3532270Google ScholarDigital Library
- Jain, A.K. and Li, SZ, 2011. Handbook of Face Recognition. New York: Springer London. https://doi.org/10.1007/978-0-85729-932-1Google ScholarCross Ref
- Martinez A.M. (2009) Face Recognition, Overview. In: Li S.Z., Jain A. (eds) Encyclopedia of Biometrics. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73003-5_84.Google ScholarCross Ref
- Kakkar, Piyush and Sharma, Vibhor. "Criminal Identification System Using Face Detection and Recognition." International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), Vol. 7, Issue 3, March 2018.Google Scholar
- M. H. Yang, D. J. Kriegman & N. Ahuja, "Detecting Faces in Images: A Survey," IEEE Transaction on Pattern Analysis & Machine Intelligence, 24:1, (2002), pp. 34-58. DOI:10.1109/34.982883Google ScholarDigital Library
- P. M. Corcoran & C. Iancu, "Automatic Face Recognition System for Hidden Markov Model Techniques," New Approaches to Characterization and Recognition of Faces, (2011). DOI:10.5772/17694Google ScholarCross Ref
- Akshat Agarwal and Ipshita Biswas. "The fundamentals of facial recognition," Technical Article, Embedded Cambridge, MA 02142 USA. Website: https://www.embedded.com/the-fundamentals-of-facial-recognition/Google Scholar
- Exadel, "Open-Source Face Recognition Service" Available: https://exadel.com/solutions/compreface/, Accessed on: March 21, 2022Google Scholar
- J. Deng, J. Guo, N. Xue, S. Zafeiriou, "ArcFace: Additive Angular Margin Loss for Deep Face Recognition", in: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR). Long Beach, CA: IEEE; 2019. pp. 4685–4694.Google ScholarCross Ref
- I. Masi, AT Tran, T. Hassner, JT Leksut, G. Medioni, "Do We Really Need to Collect Millions of Faces for Effective Face Recognition?", in: Leibe B, Matas J, Sebe N, Welling M, editors. European conference on computer vision (ECCV). Cham, Switzerland: Springer; 2016. pp. 579–596.Google Scholar
- C. Ding and D. Tao, "Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 4, pp. 1002-1014, 1 April 2018, doi: 10.1109/TPAMI.2017.2700390.Google ScholarCross Ref
- D. S. AbdELminaam, A. M. Almansori, M. Taha, and E. Badr, "A Deep Facial Recognition System using Computational Intelligent Algorithms", PLOS ONE, December 2020, doi: 10.1371/journal.pone.0242269Google ScholarCross Ref
- Khan, Zubair Ahmed and Rizvi, Asma. "AI-Based Facial Recognition Technology and Criminal Justice: Issues and Challenges." Turkish Journal of Computer and Mathematics Education, Vol.12 No.14 (2021), 3384-3392.Google Scholar
- Li, Jessica. “Labelled Faces in the Wild (LFW) Dataset”. Kaggle.com. Accessed from: https://www.kaggle.com/datasets/jessicali9530/lfw-datasetGoogle Scholar
Index Terms
- Face Recognition (FR) Integration on MABIS: A Mobile Automated Biometric Identification System for Law Enforcement in the Philippines
Recommendations
Age-Invariant Face Recognition
One of the challenges in automatic face recognition is to achieve temporal invariance. In other words, the goal is to come up with a representation and matching scheme that is robust to changes due to facial aging. Facial aging is a complex process that ...
Automatic face analysis system based on face recognition and facial physiognomy
ICHIT'06: Proceedings of the 1st international conference on Advances in hybrid information technologyAn automatic face analysis system is proposed which uses face recognition and facial physiognomy. It first detects human's face, extracts its features, and classifies the shape of facial features. It will analyze the person's facial physiognomy and then ...
Face recognition under varying illumination using gradientfaces
In this correspondence, we propose a novel method to extract illumination insensitive features for face recognition under varying lighting called the Gradientfaces. Theoretical analysis shows Gradientfaces is an illumination insensitive measure, and ...
Comments