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Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Hybrid CNN models for suspect facial recognition system

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pp. 539–551Vol. 47Issue 2February 2026DOI: 10.47974/JIOS-1606XML
Received:
11 Oct 2023
Published Online:
03 Feb 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1606
Pages:
539–551

Abstract

The criminal landscape is evolving rapidly. Perpetrators are constantly upping their technological game, exploiting new tools for nefarious purposes. Unfortunately, law enforcement isn’t always keeping pace. While a seasoned deceiver might be adept at hiding their true colors, there are often involuntary tells – especially in facial expressions. However, pinpointing these subtle clues in a bustling environment can be a real headache for authorities. Face recognition is a vital application in computer vision with many real-world use cases, such as security systems, access control, and authentication. It is worth mentioning that the Convolution Neural Network (CNN) plays an important role in speeding up and enhancing the accuracy of facial recognition algorithms to assist in surveillance systems and access control.  This project is designed to empower various authorities, including government agencies, international airports, and Egyptian border security, to enhance their investigative capabilities. by using more efficient models for face detection and recognition to build a hybrid algorithm of two CNN models to detect and predict suspicious persons and report to the authorities. Beyond facial recognition capabilities, the proposed model incorporates face detection functionalities. This will enable the identification of individuals flagged for travel restrictions or suspected of attempting unauthorized departures. This paper presents a survey of popular face recognition models using CNN and explains their features. In addition to the survey, it integrates two algorithms for detection and recognition based on a detailed analysis applied to each presented model, explaining their accuracy and the most crucial factor, processing time. The paper also introduces a practical use case that can be established in any crowded region.

Keywords

CNN (Convolutional Neural Network)Criminal investigation trackerFaceNetFace recognitionHaar cascade classifierMTCNN (Multi-task Cascaded Convolutional Networks)Surveillance

Subject Classifications

68T45 (Image processing and computer vision)94A08 (Security applications)

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