Face Detection Research Articles

Hyperspectral Imaging Database of Human Facial Skin.

The perceived color of human skin is the result of the interaction of environmental lighting with the skin. Only by resorting to human skin spectral reflectance, it is possible to obtain physical outcomes of this interaction. The purpose of this work was to provide a cured and validated database of hyperspectral images of human faces, useful for several applications, such as psychophysics-based research, object recognition, and material modeling. The hyperspectral imaging data from 29 human faces with different skin tones and sexes, under constant lighting and controlled movements, were described and characterized. Each hyperspectral image, which comprised spectral reflectance of the whole face from 400 to 720 nm in 10 nm steps at each pixel, was analyzed between and within nine facial positions located at different areas of the face. Simultaneously, spectral measurements at the same nine facial positions using conventional local point and/or contact devices were used to ascertain the data. It was found that the spectral reflectance profile changed between skin tones, subjects, and facial locations. Important local variations of the spectral reflectance profile showed that extra care is needed when considering average values from conventional devices at the same area of measurement.

Pain assessment from facial expression images utilizing Statistical Frei-Chen Mask (SFCM)-based features and DenseNet

Estimating pain levels is crucial for patients with serious illnesses, those recovering from brain surgery, and those receiving intensive care etc. An automatic pain intensity estimator is proposed in this study that gathers information about pain and intensity from the user’s expressions. The faces in the database are first cropped using a ‘Chehra’ face detector, which performs well even in wildly uncontrolled environments with a wide range of lighting and position fluctuations. The suggested technique extracts the beneficial and distinct patterns from facial expressions using novel Statistical Frei-Chen Mask (SFCM)-based features and DenseNet-based features. As it offers quick as well as accurate pain identification and pain intensity estimation, the Radial Basis Function Based Extreme Learning Machine (RBF-ELM) is employed for pain recognition and pain intensity level estimation using the characteristics. All the data is kept, updated and protected in the cloud because availability and high-performance decision-making are so important for informing physicians and auxiliary IoT nodes (such as wearable sensors). In addition, cloud computing reduces the time complexity of the training phase of Machine Learning algorithms in situations where it is possible to build a complete cloud/edge architecture by allocating additional computational resources and memory in use. The facial expression images from the UNBC-McMaster Shoulder Pain Expression Archive and 2D face dataset are used to test the proposed method. The measurement of pain intensity uses four stages. When compared to the results from the literature, the proposed work attains enhanced performance.

Explainable biometrics: a systematic literature review

AbstractBiometric systems are largely based on Machine Learning (ML) algorithms which are often considered as a black-box. There is a need to provide them with explanations to make their decision understandable. In this paper, we conduct a Systematic Literature Review aiming at investigating the present adoption of explainable Artificial Intelligence (XAI) techniques in biometric systems. By examining the biometric tasks performed by the selected papers (e.g., face detection or face spoofing), the datasets adopted by the different approaches, the considered ML models, the XAI techniques, and their evaluation methods. We started from 496 papers and, after an accurate analysis, selected 47 papers. Results revealed that XAI is mainly adopted in biometric systems related to the face biometric cues. The explanations provided were all based on model-centric metrics and did not consider how the end-users perceived the explanations, leaving wide space for the biometric researchers to apply the XAI models and enhance the explanation evaluation into an HCI perspective.

Image recommendation for social media marketing in maternity and baby care product industry – a machine learning approach

PurposeThe digital marketing landscape is rapidly evolving, but the integration of visual content still heavily depends on human expertise. Driven by the quest for innovative marketing strategies that resonate with family-oriented consumers, this study seeks to bridge this gap by applying machine learning to analyze visual content in the maternity and baby care product sector.Design/methodology/approachThis study incorporates a range of machine learning techniques – including open science framework feature detection, panoptic segmentation, customized instance segmentation, and face detection calculation methods – to analyze and predict the appeal of images, thereby enhancing user engagement and parent-child intimacy.FindingsThe exploration of various ML models, such as DT, LightGBM, RIPPER algorithm, and CNNs, has offered a comparative analysis that addresses a methodological gap in the existing literature, which frequently depends on isolated model evaluations. According to our quadrant analysis with respect to engagement rate and parent-child intimacy, the selection of a model for real-world applications depends on balancing performance and interpretability.Originality/valueThe proposed system offers a series of actionable recommendations designed to enhance customer engagement and foster brand loyalty. This study contributes to image design in maternity and baby care marketing and provides analytical insights for recommendation systems.

Enhancing Public Safety: A Comprehensive Analysis of Crime Vision Strategies

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. HAAR feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique.

AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI.

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains.

Face detection sensitivity follows the spatial distribution of experienced face locations

Face detection sensitivity follows the spatial distribution of experienced face locations

Dense pedestrian face detection in complex environments

To address the problem of dense crowd face detection in complex environments, this paper proposes a face detection model named Deep and Compact Face Detection (DCFD), which adopts an improved lightweight EfficientNetV2 network to replace the backbone network of RetinaFace. A large kernel attention mechanism is introduced to address the face detection task more accurately. The backbone network, an improved efficient channel attention (ECA) mechanism, is added to further improve the algorithm performance. The feature fusion module is an improved neural architecture search feature pyramid network (NAS-FPN) that significantly improves the face detection accuracy in different scenes. To balance the training process of positive and negative samples, we use the focus loss function to replace the traditional cross-entropy loss function. In different environments, the DCFD algorithm has shown efficient face detection performance. This algorithm provides not only a feasible and effective solution for solving the problem of face detection in dense groups but also an important basis for improving the accuracy of face detection models in practical applications.

Comparative Analysis of FaceNet, VGGFace, and GhostFaceNets Face Recognition Algorithms For Potential Criminal Suspect Identification

The escalating concerns surrounding criminal activities underscore the imperative for bolstered security measures to safeguard public welfare. Despite concerted efforts, the identification of suspects remains fraught with limitations, hindering the attainment of comprehensive individual profiles. Leveraging advancements in facial detection and identification technologies, this study assesses the efficacy of three prominent deep learning models—FaceNet, VGGFace, and GhostFaceNets—in the domain of facial recognition for suspect identification. Drawing upon data collected in 2023, the investigation scrutinizes FaceNet's intricate methodologies, including triplet loss optimization and Euclidean space mappings, yielding exceptional accuracy rates of 97.05% during validation and 97.4% during testing. Conversely, VGGFace, while displaying commendable accuracies, registers marginally lower accuracy metrics, standing at 97.05% and 96.1% during validation and testing, respectively. GhostFaceNets, integrating novel architectural components, exhibit diminished accuracy rates, signaling avenues for refinement. These empirical insights underscore FaceNet's prowess in furnishing robust and reliable facial recognition outcomes, while delineating the imperative for iterative enhancements in GhostFaceNets to foster their pragmatic applicability in security domains.

BEYOND THE ROLLCALL: SMART STRATEGY FOR ATTENDANCE

Attendance tracking is a crucial aspect of educational institutions, yet the traditional manual methods are time-consuming and error-prone. This project presents an innovative solution to this problem through the development of an automated attendance system. Utilizing advanced technologies such as face detection and optical character recognition (OCR), this system aims to streamline the attendance tracking process in schools and colleges. By automatically detecting student faces and extracting roll numbers from stickers affixed to the left side of their faces, the system eliminates the need for manual intervention, thereby improving efficiency and accuracy. Additionally, a user-friendly GUI created using customtkinter in Python enhances the system's usability and accessibility. Attendance records are automatically stored in an Excel file, allowing for easy tracking and analysis by educators.

A Mobile Surveillance System with Face Detection and Recognition Abilities

This paper proposes a mobile surveillance system similar to an electronic dog which has the ability to move around a certain location all while detecting and recognizing the individuals and notifying the respective authorities about the detected person. The system uses an Arduino UNO microcontroller along with IR receiver and remote to function as a remote-controlled vehicle while also providing an option for automatic movement of the vehicle. The system also has an integrated camera that constantly feeds live video to a computer system. Haar-cascades take in the frames of videos and detect appearance of human face. Then, pretrained FaceNet Convolutional Neural Network (CNN) compares the detected face to the authorized faces in an attempt to recognize the face. The system also boasts the feature of email notification for which it employs the SMTP protocol to send an email with the detected face to the authorized personnel. The methods and techniques used to implement these features are presented in depth in this paper.

Attention Retinex Network(A4R-Net) for face detection under low-light environment

The degradation of recognition rates in low-light environments is a critical issue in terms of security when using object and face recognition technologies in various locations. Existing low-light enhancement models have shown limitations in terms of computational cost and performance. However, this paper overcomes these limitations. The experimental results demonstrate that our model achieves the same performance as existing models with 13 times lower computational cost and a face detection performance of 82.2%.The structure of this paper is as follows: Introduction, which provides the background and explains the limitations of existing models. Proposed Method, which details the structure and working principles of A4R-Net. Experimental Results, which present the evaluation of low-light enhancement performance and the comparison of face detection using YOLOv4 [1]. Conclusion, which discusses the contributions of this research and future research directions.The source code and dataset is https://github.com/Obiru2698/obiru2698.github.io/

Unsupervised Illumination Adaptation for Low-Light Vision.

Insufficient lighting poses challenges to both human and machine visual analytics. While existing low-light enhancement methods prioritize human visual perception, they often neglect machine vision and high-level semantics. In this paper, we make pioneering efforts to build an illumination enhancement model for high-level vision. Drawing inspiration from camera response functions, our model could enhance images from the machine vision perspective despite being lightweight in architecture and simple in formulation. We also introduce two approaches that leverage knowledge from base enhancement curves and self-supervised pretext tasks to train for different downstream normal-to-low-light adaptation scenarios. Our proposed framework overcomes the limitations of existing algorithms without requiring access to labeled data in low-light conditions. It facilitates more effective illumination restoration and feature alignment, significantly improving the performance of downstream tasks in a plug-and-play manner. This research advances the field of low-light machine analytics and broadly applies to various high-level vision tasks, including classification, face detection, optical flow estimation, and video action recognition.

Face Recognition Attendance Management System using LBPH and Haar Cascade

This research presents the implementation and design of a facial recognition attendance management system by using Local Binary Pattern Histogram (LBPH) and Haar Cascade machine learning algorithms. This system aims to automate the attendance process, providing an efficient and accurate alternative to traditional methods. The Haar Cascade algorithm is employed for face detection due to its rapid processing and high detection rate, while the LBPH algorithm is utilized for face recognition because it is simple and effective in handling changes in facial expressions, lighting, occlusions, distance from the camera, and camera resolution. The integration of these algorithms results in a robust system capable of real-time attendance tracking. Experimental results demonstrate the system's high accuracy and reliability in face detection and recognition under different conditions of lighting, distance from the camera, face expressions, occlusions, and camera resolution, making it suitable for deployment in educational institutions and corporate environments.

Multifunctional FFP2 Face Mask for White Light Disinfection and Pathogens Detection using Hybrid Nanostructures and Optical Metasurfaces.

A new generation of an FFP2 (Filtering Face Piece of type 2) smart face mask is achieved by integrating broadband hybrid nanomaterials and a self-assembled optical metasurface. The multifunctional FFP2 face mask shows simultaneously white light-assisted on-demand disinfection properties and versatile biosensing capabilities. These properties are achieved by a powerful combination of white light thermoplasmonic responsive hybrid nanomaterials, which provide excellent photo-thermal disinfection properties, and optical metasurface-based colorimetric biosensors, with a very low limit of pathogens detection. The realized system is studied in optical, morphological, spectroscopic, and cell viability assay experiments and environmental monitoring of harmful pathogens, thus highlighting the extraordinary properties in reusability and pathogens detection of the innovative face mask.

Design and implementation of a driving safety assistant system based on driver behavior

<p>These days, road accidents are one of Morocco's biggest problems. Fatigue, drowsiness, and driver behavior are among the primary causes.This research aims to develop an embedded system by image processing and computer vision to ensure driving safety by monitoring driver behavior and assist drivers to awaken from micro-sleep or fatigue due to long driving hours and various other reasons. Indeed, the driver inattention, drowsiness or driver fatigue can be detected. The suggested method is designed to support drivers if needed, based on the vehicle velocity. Once the driver crosses a certain speed limit, the program starts face detection and analyzing this data to determine whether the driver is tired, sleepy, or inattentive. This activates different alarm depending on the criticality level. It can sound a voice alert to help him wake up and drive more cautiously. The system is based on AI algorithms in image processing based on OpenCV libraries and the Python language to capture the movements of the driver's eyes and head when starting the automobile. Every algorithm is run on a Raspberry-Pi 4 card, and numerous experimentation series have demonstrated overall credible performance with success accuracy of over 93% in EAR and MAR calculations.</p>

Quarantine Order Violators System using Face Recognition (FACID)

Face recognition technology is commonly utilized for user authentication and verification by analysing a digital image of a person's face and matching it against a database of faces for identification purposes. The COVID-19 pandemic has led to mandatory quarantines and the use of quarantine bracelets for some individuals, which can be time-consuming and require a lot of effort. Face recognition technology can help raise awareness about the current situation and alleviate some of the burden associated with quarantine measures. The aim of this research is to concentrate on the face recognition of individuals with a history of quarantine, as a measure to prevent the spread of COVID-19 in educational institutions like universities, colleges, and schools. This research study concentrates on individuals with a history of quarantine orders. The system will employ the Histograms of Oriented Gradients (HOG) algorithm for face detection. Additionally, the system will utilize the Face Landmark Algorithm to compare the 128-d vector with images stored locally. The system will make use of the Helen face collection dataset for its data requirements. The aim of this study is to explore and identify techniques for detecting individuals who violate quarantine measures and issuing notifications through the proposed system. By implementing this system, it could contribute to creating a more secure environment within the educational institution and potentially reduce the spread of the virus.

Facial detection of formaldehyde by using acidichromic carbon dots and the reaction between formaldehyde and ammonium chloride

Herein we report the facial detection of formaldehyde (FA) by using an interesting red acidichromic carbon dots (ACDs) which turns blue when pH gradually decreases. The color change was attributed to the conversion between the double bonds (C=N) and single bonds (C-N) on the surface of the ACDs. Inspired by the reaction between FA and ammonium chloride that produces H+ and methenamine and decrease the pH value of the solution, a fast and simple visual detection method for FA was found with a minimum discriminated concentration of 0.04 mol/L. A fluorescence detection method for FA was also found with LOD of 0.029 mol/L and FA in real sample, e.g., shredded squid was successfully analyzed. This work provides a new idea of developing fast visual detection method for daily monitor or in-site semiquantitative assessment on FA.

Real-Time Human Profiling: Unveiling Age, Gender and Emotions Using Deep Learning

This paper introduces the development of a real-time system, which deploys an integrated use of Flask, deep learning, Convolutional Neural Networks (CNNs) and cascade classifier approach to detect age, gender, and emotion from facial images. While its numerous applications—ranging from marketing and medical services to security surveillance—immediately catches the eye, the facial recognition technology is fast becoming the hot topic. Our suggested system aims at absolutely determining the age, gender or emotional state of a person instantaneously in real life. Flask, Python micro web framework, is at the basis of the software, providing the desired functionalities for the exchange of information between the processing engine and the front end. Convolutional Neural Networks (CNNs) which are a part of deep learning for tasks involving feature extraction and classification are the main tool used by most learning algorithms. CNNs are widely used in face recognition systems mainly due to the fact that they perform very well at such tasks as image processing. On the one hand, the model uses cascade classifiers for superior face detection, thereby finding and separating face regions in input images or video streams. Unlike some of the approaches that require heavy computation, these classifiers are computationally light solutions that can run in real-time even on resource- limited devices. The system capacity to identify age, gender, and emotion accurately in real-time is exemplified through: performance evaluation. We have used various means to stress the system and ensure that it is precise and offers timely results over the given period. By means of varied testing stages, we have discovered that the system usually brings high levels of precision and validity for the numerous data sets and trial situations. In this regard, be it differentiating between individuals’ age or readily identifying the exact gender or recognizing nuanced emotional clues, the system performs at the optimum level.

Study of Multimodal Identification Algorithms using Modern Methods and Tools of Multivariate Analysis

This article aims to comprehensively investigate the theoretical and practical foundations, as well as the distinctive characteristics, underpinning the study of multimodal identification algorithms. This investigation will be conducted using state-of-the-art methods and tools of multidimensional analysis. The development of a multimodal algorithm using the method of modality fusion at the feature level encompasses the integration of various algorithms rooted in multivariate analysis. These include a combined voice activity detector, a face detector utilizing the MTCNN (multi-task cascade convolutional networks) architecture, fine-frequency cepstral coefficients, facial image features, and a decision-making module. To construct a multimodal identification algorithm, a framework for combining these algorithms based on multivariate analysis is proposed. Analysis of the acquired data indicates that “Test 1”, utilizing facial image data, exhibits the highest performance indicators, approaching nearly 100%. Tests 2 and 3 involving voice signals exhibit a minor error in the pre-processing stage, attributed to the inherent delay experienced by participants during the video conference. The proposed multimodal algorithm, integrated within a biometric identification system, enables successful user verification research through the utilization of a combined multidimensional analysis algorithm. Furthermore, the algorithm showcases superior research outcomes in comparison to other analogous multimodal identification algorithms, as it yields precise results.