Thaer Thaher^1,*, Muhammed Saffarini², Majdi Mafarja³, Abdulaziz Alashbi⁴, Abdul Hakim Mohamed⁵, Ayman A. El-Saleh⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2359-2394, 2025, DOI:10.32604/cmes.2025.065388 - 31 August 2025

Abstract Face detection is a critical component in modern security, surveillance, and human-computer interaction systems, with widespread applications in smartphones, biometric access control, and public monitoring. However, detecting faces with high levels of occlusion, such as those covered by masks, veils, or scarves, remains a significant challenge, as traditional models often fail to generalize under such conditions. This paper presents a hybrid approach that combines traditional handcrafted feature extraction technique called Histogram of Oriented Gradients (HOG) and Canny edge detection with modern deep learning models. The goal is to improve face detection accuracy under occlusions. The… More >

Thaer Thaher^1,*, Majdi Mafarja², Muhammed Saffarini³, Abdul Hakim H. M. Mohamed⁴, Ayman A. El-Saleh⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2615-2673, 2025, DOI:10.32604/cmes.2025.064857 - 30 June 2025

Abstract Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks, sunglasses, and other obstructions. Addressing this issue is crucial for applications such as surveillance, biometric authentication, and human-computer interaction. This paper provides a comprehensive review of face detection techniques developed to handle occluded faces. Studies are categorized into four main approaches: feature-based, machine learning-based, deep learning-based, and hybrid methods. We analyzed state-of-the-art studies within each category, examining their methodologies, strengths, and limitations based on widely used benchmark datasets, highlighting their adaptability to partial and severe occlusions. The review… More >

Mahmood Ul Haq¹, Muhammad Athar Javed Sethi¹, Sadique Ahmad², Naveed Ahmad³, Muhammad Shahid Anwar^4,*, Alpamis Kutlimuratov⁵

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1-24, 2025, DOI:10.32604/cmc.2025.063341 - 09 June 2025

Abstract Face recognition has emerged as one of the most prominent applications of image analysis and understanding, gaining considerable attention in recent years. This growing interest is driven by two key factors: its extensive applications in law enforcement and the commercial domain, and the rapid advancement of practical technologies. Despite the significant advancements, modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions, occlusion, and diverse facial postures. In such scenarios, human perception is still well above the capabilities of present technology. Using the systematic mapping study, this paper presents an in-depth review More >

Muhamad Irsan^1,2,*, Rosilah Hassan², Anwar Hassan Ibrahim³, Mohamad Khatim Hasan², Meng Chun Lam², Wan Mohd Hirwani Wan Hussain⁴

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 157-174, 2024, DOI:10.32604/iasc.2024.042193 - 21 May 2024

Abstract One of the major causes of road accidents is sleepy drivers. Such accidents typically result in fatalities and financial losses and disadvantage other road users. Numerous studies have been conducted to identify the driver’s sleepiness and integrate it into a warning system. Most studies have examined how the mouth and eyelids move. However, this limits the system’s ability to identify drowsiness traits. Therefore, this study designed an Accident Detection Framework (RPK) that could be used to reduce road accidents due to sleepiness and detect the location of accidents. The drowsiness detection model used three facial… More >

Latifah Almuqren¹, Manar Ahmed Hamza^2,*, Abdullah Mohamed³, Amgad Atta Abdelmageed²

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 4917-4933, 2023, DOI:10.32604/cmc.2023.037738 - 29 April 2023

Abstract Face recognition technology automatically identifies an individual from image or video sources. The detection process can be done by attaining facial characteristics from the image of a subject face. Recent developments in deep learning (DL) and computer vision (CV) techniques enable the design of automated face recognition and tracking methods. This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking (HHODL-AFDT) method. The proposed HHODL-AFDT model involves a Faster region based convolution neural network (RCNN)-based face detection model and HHO-based hyperparameter optimization process. The presented optimal Faster RCNN model… More >

Ching-Bang Yao¹, Chang-Yi Kao^2,*, Jiong-Ting Lin³

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2233-2252, 2023, DOI:10.32604/iasc.2023.034488 - 05 January 2023

Abstract Traditional monitoring systems that are used in shopping malls or community management, mostly use a remote control to monitor and track specific objects; therefore, it is often impossible to effectively monitor the entire environment. When finding a suspicious person, the tracked object cannot be locked in time for tracking. This research replaces the traditional fixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person. This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.… More >

S. Sandhya¹, A. Balasundaram^2,*, Ayesha Shaik¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2331-2343, 2023, DOI:10.32604/cmc.2023.032715 - 31 October 2022

Abstract Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade. One of the most tedious tasks is to track a suspect once a crime is committed. As most of the crimes are committed by individuals who have a history of felonies, it is essential for a monitoring system that does not just detect the person’s face who has committed the crime, but also their identity. Hence, a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network… More >

Amal H. Alharbi¹, S. Karthick², K. Venkatachalam³, Mohamed Abouhawwash^4,5, Doaa Sami Khafaga^1,*

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2773-2787, 2023, DOI:10.32604/iasc.2023.030763 - 17 August 2022

Abstract Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security techniques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame… More >

Ahmed M. Ayoup^1,*, Ashraf A. M. Khalaf¹, Fahad Alraddady², Fathi E. Abd El-Samie³, Walid El-Shafai^3,5, Salwa M. Serag Eldin^2,4

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1289-1304, 2022, DOI:10.32604/iasc.2022.024381 - 08 February 2022

Abstract In this article, we introduce a new cancelable biometric template generation layout depending on selective encryption technology and Dual-Tree Complex Wavelet Transform (DT-CWT) fusion. The input face biometric is entered into the automatic face-segmentation (Viola-Jones) algorithm to detect the object in a short time. Viola-Jones algorithm can detect the left eye, right eye, nose, and mouth of the input biometric image. The encoder can choose the left or right eye to generate a cancelable biometric template. The selected eye image of size M × N is XORed with the created pseudo-random number (PRN) matrix C_{M × N} to… More >

P. Karuppanan^1,*, K. Dhanalakshmi²

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1025-1043, 2022, DOI:10.32604/iasc.2022.023712 - 08 February 2022

Abstract Facial recognition is a trending technology that can identify or verify an individual from a video frame or digital image from any source. A major concern of facial recognition is achieving the accuracy on classification, precision, recall and F1-Score. Traditionally, numerous techniques involved in the working principle of facial recognition, as like Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Subspace Decomposition Method, Eigen Feature extraction Method and all are characterized as instable, poor generalization which leads to poor classification. But the simplified method is feature extraction by comparing the particular facial features of the… More >