Face Detection Research Articles

Thumbnail-Preserving Encryption Technology Based on Digital Processing

In recent years, the security of cloud storage has become a topic attracting significant attention due to a series of features such as large storage space, high availability, and low cost. Although traditional plain text images can withstand external attacks, the usability of images is completely lost. In order to balance the usability and privacy of images, some scholars have proposed the thumbnail-preserving encryption (TPE) scheme. The ideal TPE algorithm can keep the same thumbnail before and after encryption, which reduces the time cost and strengthens the resistance to attacks, but the existing schemes cannot fulfill the above criteria. In this paper, we propose a new TPE scheme that combines bit-transform encryption and improved hierarchical encryption. By constructing a chaotic system, both encryption and decryption times are shortened, while the randomness of the selected cells is enhanced. In addition, the Hamming distance is introduced to classify and scramble the binary encryption units. The experimental results show that when the number of thumbnail chunks is 16 × 16, the encryption and decryption time decreases to 4 s, and the SSIM value after encryption is close to 1, which indicates that the thumbnail before and after satisfying the encryption basically remains the same, and when the number of chunks is gradually increased, the success rate of the face detection tends to be close to 0. In addition, as the number of experimental iterations increases, the encryption effect improves with an increasing ability to resist attacks.

Face Recognition based Attendance System

In today’s era, facial recognition emerges as a pivotal tool, offering security, authentication, and identification benefits across various sectors. This paper introduces an automated attendance system that incorporates real-time face recognition technology. The system aims to overcome the limitations and inefficiencies associated with traditional manual attendance methods, particularly in educational institutions and workplace environments. Two approaches are outlined: one utilizing local servers and AWS cloud recognition API, and the other relying solely on AWS resources for processing. Both approaches entail constructing a database of individuals’ images and utilizing algorithms such as Haar-Cascade and Local Binary Pattern Histogram for face detection and recognition. By automating attendance through live video streams, the system mitigates issues like proxy attendance and inaccuracies. Technologies such as OpenCV, dlib, and MySQL are leveraged to streamline implementation. This integration of face recognition with cloud computing promises to revolutionize attendance tracking, offering scalability, efficiency and accountability.

An Improved Image Enhancement Technique for Low Light Images Using Deep Learning Approach

Image enhancement in facial detection is a critical component of facial recognition systems. Face identification in an uncontrolled environment is affected by a multitude of difficulties such as poor light levels, low-resolution cameras, occlusions from surrounding objects, and tiny faces in distant photographs. Low signal-to-noise ratio, low brightness, and noise in low-light photographs lead to issues such as color distortion and poor visibility, which makes it challenging to identify faces. Many techniques to enhance low-light images have been developed, improving the face detection system’s accuracy. This will improve the picture at the expense of higher running costs and lower model robustness. The proposed technique, DCE-Net, uses performance-intensive deep learning and light-enhanced image properties. A non-referential deep learning technique was employed to acquire and modify the image attributes. A set of loss functions designed to perform without ground-truth images is the foundation of the deep network learning employed. Compared to the current referential methods, straightforward non-referential light estimation curve mapping minimizes the computational demand for low-light image improvement. Several experiments conducted on standard datasets demonstrated the efficacy and reduced computational requirements of the approach. The effectiveness of this method is supported by both the qualitative and quantitative outcomes. The PSNR and SSIM computation for paired images shows promising results using the proposed image enhancement technique.

A Face Detection System: A Comprehensive Survey

The domain of facial detection has witnessed significant advancement, mostly attributed to the progress in machine learning and deep learning methodologies. These enhancements have resulted in a fundamental change in the field. Face detection algorithms are extensively utilized in several domains, such as security, surveillance, and social networking applications. This survey has thoroughly investigated various methodologies for facial identification, with a focus on the challenges, applications, and evolution from traditional to deep learning-based approaches. Furthermore, it provides important insights into commonly used datasets for facial detection, including a comprehensive examination of their unique characteristics. The study is structured sequentially, commencing with an introduction to the assessment of significant previous research and the challenges faced by face detection algorithms. Subsequently, the investigation delves into different practical uses, delineates methods for identifying, and scrutinizes frequently employed data sets. The transition from traditional classifiers to deep learning models in face identification technology represents a significant improvement in addressing the complexities of face detection in various environments. Despite the obstacles that now exist, the continuous advancements in deep learning offer promising solutions that enhance the accuracy and efficiency of face identification systems in a wide range of applications.

CCNN-SVM: Automated Model for Emotion Recognition Based on Custom Convolutional Neural Networks with SVM

The expressions on human faces reveal the emotions we are experiencing internally. Emotion recognition based on facial expression is one of the subfields of social signal processing. It has several applications in different areas, specifically in the interaction between humans and computers. This study presents a simple CCNN-SVM automated model as a viable approach for FER. The model combines a Convolutional Neural Network for feature extraction, certain image preprocessing techniques, and Support Vector Machine (SVM) for classification. Firstly, the input image is preprocessed using face detection, histogram equalization, gamma correction, and resizing techniques. Secondly, the images go through custom single Deep Convolutional Neural Networks (CCNN) to extract deep features. Finally, SVM uses the generated features to perform the classification. The suggested model was trained and tested on four datasets, CK+, JAFFE, KDEF, and FER. These datasets consist of seven primary emotional categories, which encompass anger, disgust, fear, happiness, sadness, surprise, and neutrality for CK+, and include contempt for JAFFE. The model put forward demonstrates commendable performance in comparison to existing facial expression recognition techniques. It achieves an impressive accuracy of 99.3% on the CK+ dataset, 98.4% on the JAFFE dataset, 87.18% on the KDEF dataset, and 88.7% on the FER.

Student Organization Website with E-Voting Feature by Using Student Card Verification Concept Design

Student organizations hold an election to decide their next head and vice head every year. The best voting method for student organizations is to use an independent website with a voting system. The voting system can use students’ identity card and their student email as base for verification. OCR and face detection can be used for extracting all the needed information to validate the student card and verify it with the corresponding student email input. Other than the voting system, the website can be used to promote the student organization itself. The website was built using Nuxt for its front-end, Firebase for its back-end, and Cloud Vision API for its OCR and face detection module. There is a Lighthouse test, a stress test for the voting system, and a test to determine the optimal file size for the voting system. The results are a website that has an average Lighthouse score of 97.58. The stress test, which used a script that does submission repeatedly, results suggest that the voting system can handle up to 2000 voters at the same time. The optimal file size determined by the authors to be 500KB as the result of its test. The conclusions are a great performing website with a voting system can be built using Nuxt and Firebase, the voting system can be improved by adding another step of verification, and it’s best to use and image with a file size above 250KB when using Cloud Vision API for optimal results

Face Recognition Using LBPH and CNN

Objective:: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security; however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods:: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results:: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion:: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result.

Smart Recognition and Authentication based on Haar Cascade Classifier and Visual Cryptography

The algorithm with an E-verification method is proposed that combines automated facial recognition technology with digital media and later it combines aspects of secret data exchange and visual cryptography. A face detection technique based on Internet of Things (IoT) selects features using the Principal Component Analysis (PCA) algorithm and the Haar cascade classifier. The selected face serving as a cover follows two level Discrete Haar Wavelet Transform. The shares generated from digital signature and fingerprint are diffused into the converted coefficients. Furthermore, the imperceptibility of the additional noise is increased by a bit-level noise reduction technique. Authenticity is confirmed by regenerating a message digest at the receiving end, and the extraction process operates in complete blindness. The approach is suitable for smart card design and may be used as an automatic recognition system in a real-world setting. Performance comparisons show a notable improvement over other approaches that are comparable. Additionally, the technique is effective against some related attacks.

Face Recognition Based Attendance System using ESP 32CAM

Abstract: In the modern industrial system, attendance logging is essential to raising system quality. Thus, it is necessary to have a robust computerized Face detection attendance recording system that uses an ESP32 camera. Each and every organization has strict attendance policies. It takes time and effort to manually maintain an attendance register. For example, RFID and biometric technology are two ways of solving the same issue. This research outlines a creative and efficient method for determining presence and verifying attendance. The ESP32 microcontroller family features integrated Wi-Fi and dual-mode Bluetooth. This facial detection-based technology does not require a person to be in close proximity to the fingerprint reader module, in contrast to biometric attendance systems. The biometric device becomes contaminated when the same employee or student records their attendance using the same biometric system. Furthermore, the likelihood of a proxy in RFID is considerable. This forecast will track student or employee attendance using facial recognition technology.

Wheelchair Navigation Enhanced by Machine Learning Eye-Gaze Control

Abstract: This study focuses on the development of an eye-controlled wheelchair system tailored for individuals with motor disabilities, with the overarching goal of augmenting their mobility and autonomy. A multifaceted approach was adopted, incorporating meticulous data collection and preprocessing techniques, notably encompassing real-time face detection and feature extraction. Various machine learning algorithms, including KNN, Decision Trees, Random Forests, and SVM, were meticulously trained to anticipate user commands. Through rigorous performance evaluation, the system demonstrated remarkable accuracy and precision, with KNN and SVM emerging as the top-performing models. Notably, this innovative system exhibits significant potential in enhancing accessibility and fostering independence, thereby exemplifying notable strides in the realm of assistive technology. The integration of advanced data processing methodologies, machine learning techniques, and real-time prediction mechanisms facilitates a seamless and intuitive navigation experience, thus holding promise for substantially enriching the quality of life for individuals grappling with mobility disabilities. This research underscores the pivotal role of interdisciplinary collaboration and technological innovation in devising solutions that cater to the diverse needs of individuals with disabilities, thereby advocating for inclusivity and empowerment in society.

Development of a Face Recognition System for Authentication in Computer-Based Examinations: A Comparison with Fingerprint Verification

This study aims to overcome the limitations of fingerprint biometric authentication in computer-based examinations by introducing a robust face recognition system. The research targets key challenges such as administrative overhead, prolonged authentication times, and issues with false positives and negatives. By leveraging advanced technologies like Multi-Task Cascaded Convolutional Neural Network (MTCNN) for precise facial detection and FaceNet for efficient facial feature embedding, the developed system achieves substantial improvements across critical metrics. The evaluation metrics consistently report high ratings: accuracy 90%, speed 92%, ease of use 86%, robustness to variations 94%, security 92%, and privacy 90%. Evaluation of the system shows its superiority over traditional fingerprint methods. It demonstrates notably higher accuracy, efficiency, user-friendliness, adaptability to diverse conditions, and enhanced levels of security and privacy. These advancements position the face recognition system as a compelling alternative for enhancing the authentication processes in computer-based examinations. Keywords: FaceNet, Multi-Task Cascaded Convolutional Neural Network, Face Recognition, Security, Face, Fingerprint Detection. Sulaimon O.O., Olabiyisi, S.O. & Ismaila, W.O. (2024): Development of a Face Recognition System for Authentication in Computer-Based Examinations: A Comparison with Fingerprint Verification. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 2. Pp 117-128. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N2P8

Student Face Recognition and Alert Rule

Abstract: Attendance marking in a classroom during a lecture is not only an onerous task but also a time-consuming one at. Due to an unusually high number of students present during the lecture, there will always be a probability of proxy attendance(s). Attendance marking with conventional methods has been an area of challenge. The growing need for efficient and automatic techniques for marking attendance is a growing challenge in the area of face recognition. In recent years, the problem of automatic attendance marking has been widely addressed through the use of standard biometrics like fingerprint and Radiofrequency Identification tags, etc., However, these techniques lack the element of reliability. In this proposed project an automated attendance marking and management system is proposed by making use of face detection and recognition algorithms. Instead of using conventional methods, this proposed system aims to develop an automated system that records the student’s attendance by using facial recognition technology. The main objective of this work is to make the attendance marking and management system efficient, time-saving, simple, and easy. Here faces will be recognized using face recognition algorithms. The processed image will then be compared against the existing stored record and then attendance is marked in the database accordingly. Compared to the existing system traditional attendance marking system, this system reduces the workload of people. This proposed system will be implemented with 4 phases Image Capturing, Segmentation of group image and Face Detection, Face comparison and Recognition, and Updating of Attendance in the database. Index Term – Mongo DB, React JS, Node JS, Express JS, Attendance, Facial recognition, and detection, Haar cascade, Alert, Student Face.

GAN-Generated Face Detection Based on Multiple Attention Mechanism and Relational Embedding

The rapid development of the Generative Adversarial Network (GAN) makes generated face images more and more visually indistinguishable, and the detection performance of previous methods will degrade seriously when the testing samples are out-of-sample datasets or have been post-processed. To address the above problems, we propose a new relational embedding network based on “what to observe” and “where to attend” from a relational perspective for the task of generated face detection. In addition, we designed two attention modules to effectively utilize global and local features. Specifically, the dual-self attention module selectively enhances the representation of local features through both image space and channel dimensions. The cross-correlation attention module computes similarity between images to capture the global information of the output in the image. We conducted extensive experiments to validate our method, and the proposed algorithm can effectively extract the correlations between features and achieve satisfactory generalization and robustness in generating face detection. In addition, we also explored the design of the model structure and the inspection performance on more categories of generated images (not limited to faces). The results show that RENet also has good detection performance on datasets other than faces.

How Ethical Is It to Rely on Artificial Intelligence with Biased Facial Recognition?

This paper examines the ethics and current situation of facial recognition from the perspective of racial and gender equality. Facial recognition is a fairly new image analytics technology. It consists of face detection, which dissociates the face from the background, and facial recognition, which compares unique features of an individual’s face to a database and biometrics of sample faces. The primary ethical concern with this technology is biased teaching. Statistics show that a darker-skinned female is significantly less likely to be detected and recognized with the correct identity than a lighter-skinned male. Many works on this issue, some mentioned and analyzed in this paper, underscore the morally and ethically problematic use of this technology in a wide range of important fields considering that facial recognition technology is still in its infancy, and the consequences of misidentification can be severe.

Frequency and associated factors of mask-induced acne among healthcare workers

Background: Mask-induced acne, also known as maskne, is a skin condition that has become increasingly prevalent during the COVID-19 pandemic. The prolonged use of facial masks has been found to cause skin irritation, inflammation, and the development of acne. Healthcare workers are at an increased risk due to their prolonged and consistent use of facial masks during their work. Objective: To investigate the occurrence and identify risk factors of mask induced acne among health care workers. Patients and Methods: This cross-sectional study included 115 healthcare workers at Al_Imamain Al_Kadhumain Medical city who wore the mask daily. Collected data include demographic characteristics (age, sex, and occupation), mask-wearing data include the type of mask worn, the pattern of mask use (disposable or reusable mask), time spent wearing masks over the years, the daily duration of mask usage, and specifics regarding participants' acne, encompassing the type and facial location of the acne, as well as accompanying symptoms (itching, dryness, oiliness, moisture, warmth, and excessive facial sweating), and factors that exacerbate the condition, were all considered in the study. Results: Out of 115 included subjects 37 (32.17%) were suffering from new-onset mask induce acne. There were 10 men and 27 women. The mean age was 35.5±7.3 years (range: 24-47). The vast majority of the included participants (81.08%) were wearing surgical masks. The duration of mask use in about three-fourths of patients was ≥ one year. About three-fourth of patients were using reusable mask. Pustules were the most common lesion, accounting for two-thirds of the participants, followed by comedones (37.83%). The most common site was cheeks (75.67%), followed by the chin (48.65%). Stress was the most common aggravating factor occurred in 37.84% of the participants, followed by high-glycemic diet and hot weather (27.03% each). Conclusion: Female, prolonged duration of mask use and wearing reusable masks could be associated with the development of maskne. Stress and a glycemic diet could aggravate the lesion associated with maskne.

Frequency and Associated Factors of Mask-Induced Acne among Healthcare Workers

Background: Mask-induced acne, also known as maskne, is a skin condition that has become increasingly prevalent during the COVID-19 pandemic. The prolonged use of facial masks has been found to cause skin irritation, inflammation, and the development of acne. Healthcare workers are at an increased risk due to their prolonged and consistent use of facial masks during their work. Objective: To investigate the occurrence and identify risk factors of mask induced acne among health care workers. Patients and Methods: This cross-sectional study included 115 healthcare workers at Al_Imamain Al_Kadhumain Medical city who wore the mask daily. Collected data include demographic characteristics (age, sex, and occupation), mask-wearing data include the type of mask worn, the pattern of mask use (disposable or reusable mask), time spent wearing masks over the years, the daily duration of mask usage, and specifics regarding participants' acne, encompassing the type and facial location of the acne, as well as accompanying symptoms (itching, dryness, oiliness, moisture, warmth, and excessive facial sweating), and factors that exacerbate the condition, were all considered in the study. Results: Out of 115 included subjects 37 (32.17%) were suffering from new-onset mask induce acne. There were 10 men and 27 women. The mean age was 35.5±7.3 years (range: 24-47). The vast majority of the included participants (81.08%) were wearing surgical masks. The duration of mask use in about three-fourths of patients was ≥ one year. About three-fourth of patients were using reusable mask. Pustules were the most common lesion, accounting for two-thirds of the participants, followed by comedones (37.83%). The most common site was cheeks (75.67%), followed by the chin (48.65%). Stress was the most common aggravating factor occurred in 37.84% of the participants, followed by high-glycemic diet and hot weather (27.03% each). Conclusion: Female, prolonged duration of mask use and wearing reusable masks could be associated with the development of maskne. Stress and a glycemic diet could aggravate the lesion associated with maskne. Keywords: Maskne, acne lesion, aggravating factors.

YOLO-FaceV2: A scale and occlusion aware face detector

In recent years, face detection algorithms based on deep learning have made great progress. Nevertheless, the effective utilization of face detectors for small and occlusion faces remains challenging, primarily stemming from the limitations in pixel information and the presence of missing features. In this paper, we propose a novel real-time face detector, YOLO-FaceV2, built upon the YOLOv5 architecture. Our approach introduces a Receptive Field Enhancement (RFE) module designed to extract multi-scale pixel information and augment the receptive field for accurately detecting small faces. To address issues related to face occlusion, we introduce an attention mechanism termed the Separated and Enhancement Attention Module (SEAM), which effectively focuses on the regions affected by occlusion. Furthermore, we propose a Slide Weight Function (SWF) to mitigate the imbalance between easy and hard samples. The experiments demonstrate that our YOLO-FaceV2 achieves performance exceeding the state-of-the-art on the WiderFace validation dataset. Source code and pre-trained model are available at https://github.com/Krasjet-Yu/YOLO-FaceV2.

Facial Emotion Recognition System Using CNN for Song Mapping

This project introduces a novel system that integrates Facial Emotion Recognition (FER) with music mapping to enhance human-computer interaction. Leveraging a custom Convolutional Neural Network (CNN) algorithm, developed using the FER2013 dataset, we classify emotions into four categories- happy, sad, neutral, and angry- employing Haar Cascade for precise face detection and grayscale conversion for optimal CNN input. Our custom CNN demonstrates superior performance, achieving a testing accuracy of 77.23%, notably surpassing established models like VGG16 and ResNet50, which achieved 55.87% and 62.76% respectively. This system swiftly identifies emotions and recommends songs from the "278k Emotion Labeled Spotify Songs" playlist, aiming to boost user satisfaction. Through nuanced comparisons with VGG16 and ResNet50, our approach underscores its inherent strengths, suggesting promising advancements in Facial Emotion Recognition (FER) and music recommendation systems. With a focus on precision in emotion detection, our system subtly elevates user experience, contributing meaningfully to ongoing research in the field.

Perception of Artificial Intelligence: GSR Analysis and Face Detection

Perception of Artificial Intelligence: GSR Analysis and Face Detection

An efficient ensemble explainable AI (XAI) approach for morphed face detection

Numerous deep neural convolutional architectures have been proposed in literature for face Morphing Attack Detection (MADs) to prevent such attacks and lessen the risks associated with them. Although, deep learning models achieved optimal results in terms of performance, it is difficult to understand and analyze these networks since they are black box/opaque in nature. As a consequence, incorrect judgments may be made. There is, however, a dearth of literature that explains decision-making methods of black box deep learning models for biometric Presentation Attack Detection (PADs) or MADs that can aid the biometric community to have trust in deep learning-based biometric systems for identification and authentication in various security applications such as border control, criminal database establishment etc. In this work, we present a novel visual explanation approach named Ensemble XAI integrating Saliency maps, Class Activation Maps (CAM) and Gradient-CAM (Grad-CAM) to provide a more comprehensive visual explanation for a deep learning prognostic model (EfficientNet-B1) that we have employed to predict whether the input presented to a biometric authentication system is morphed or genuine. The experimentations have been performed on three publicly available datasets namely Face Research Lab London (FRLL) dataset, Wide Multi-Channel Presentation Attack (WMCA) dataset, and Makeup Induced Face Spoofing (MIFS) dataset. The experimental evaluations affirms that the resultant visual explanations highlight more fine-grained details of image features/areas focused by EfficientNet-B1 to reach decisions along with appropriate reasoning.