Face Detection Process Research Articles

Implementation of the YOLO Method for Detection of Human Emotions Based on Facial Mimics

Emotion detection through facial expression recognition plays an important role in everyday life, such as how to respond correctly to emotional expressions in social interactions, so that you can establish and build verbal or nonverbal communication with other people and so on. Facial expressions are facial changes in response to a person's emotional state, intentions, or social communication. Face detection is the first step that must be taken in facial analysis, including facial expression recognition. There are many methods that can be used to carry out the face detection process, such as the YOLO method. This YOLO method reframes object detection as a single regression problem, directly from image pixels to bounding box coordinates and class probabilities. By using the YOLO method, the process only needs to look once at the input image, to predict what objects are in the image and where those objects are. Based on the results of the tests carried out, the YOLO method can be used to detect human facial expressions with a success rate of 80%, with neutral, surprise and disgust facial expressions having a good level of accuracy and fear facial expressions having a poor accuracy level. The YOLO method is able to detect facial expressions of humans who wear accessories such as glasses.

GCS-YOLOv8: A Lightweight Face Extractor to Assist Deepfake Detection.

To address the issues of target feature blurring and increased false detections caused by high compression rates in deepfake videos, as well as the high computational resource requirements of existing face extractors, we propose a lightweight face extractor to assist deepfake detection, GCS-YOLOv8. Firstly, we employ the HGStem module for initial downsampling to address the issue of false detections of small non-face objects in deepfake videos, thereby improving detection accuracy. Secondly, we introduce the C2f-GDConv module to mitigate the low-FLOPs pitfall while reducing the model's parameters, thereby lightening the network. Additionally, we add a new P6 large target detection layer to expand the receptive field and capture multi-scale features, solving the problem of detecting large-scale faces in low-compression deepfake videos. We also design a cross-scale feature fusion module called CCFG (CNN-based Cross-Scale Feature Fusion with GDConv), which integrates features from different scales to enhance the model's adaptability to scale variations while reducing network parameters, addressing the high computational resource requirements of traditional face extractors. Furthermore, we improve the detection head by utilizing group normalization and shared convolution, simplifying the process of face detection while maintaining detection performance. The training dataset was also refined by removing low-accuracy and low-resolution labels, which reduced the false detection rate. Experimental results demonstrate that, compared to YOLOv8, this face extractor achieves the AP of 0.942, 0.927, and 0.812 on the WiderFace dataset's Easy, Medium, and Hard subsets, representing improvements of 1.1%, 1.3%, and 3.7% respectively. The model's parameters and FLOPs are only 1.68 MB and 3.5 G, reflecting reductions of 44.2% and 56.8%, making it more effective and lightweight in extracting faces from deepfake videos.

Facial Recognition Based Automated Door

To ensure the accuracy and efficiency of intruder identification, the proposed method is combined with Haar clssifier technology for face detection. When someone comes to the door, the Pi camera captures the image and starts the face detection process. In this research, we implement a facial recognition component to capture human images, comparing them with stored data in a database. Upon a match with an authorized individual, the system unlocks the door through an electromagnetic lock. The demand for a rapid and accurate face recognition system persists, continuously evolving to swiftly identify intruders and restrict unauthorized access to highly secure areas, thereby reducing human error. Facial recognition stands as a crucial component within secure systems, surpassing biometric pattern recognition methods, and finds widespread application across various domains.

Evaluation Method of Real-Time Face Detection

Object detection is one of computer technology using image or video. Face detection is closely related Image processing and computer vision are used to detect several objects including human faces, landscapes, cars, etc. Face detection algorithm aims to confirm if an image has a face as the object in it. In this study, face detection uses several methods, namely the Eigenface method, the Fisherface method, and the Local Binary Pattern Histogram (LBPH) method. This study used 10 different subjects. The test was carried out 15 times using each face detection method with constant distance. The face detection process in this study was simulated using JupyterLab. The result showed that LBPH method obtained the highest level of accuracy in between comparison among Fisherface method and the Eigenface method. The accuracy of the LBPH method is 93.90%, while the Eigenface method is 85% and the Fisherface method is 53.33%. Differences in face detection accuracy were found due to the low level of lighting in the room and the use of accessories on the subject.

Understanding face detection with visual arrays and real-world scenes

ABSTRACT Face detection has been studied by presenting faces in blank displays, object arrays, and real-world scenes. This study investigated whether these display contexts differ in what they can reveal about detection, by comparing frontal-view faces with those shown in profile (Experiment 1), rotated by 90° (Experiment 2), or turned upside-down (Experiment 3). In blank displays, performance for all face conditions was equivalent, whereas upright frontal faces showed a consistent detection advantage in arrays and scenes. Experiment 4 examined which facial characteristics drive this detection advantage by rotating either the internal or external facial features by 90° while the other features remained upright. Faces with rotated internal features were detected as efficiently as their intact frontal counterparts, whereas detection was impaired when external features were rotated. Finally, Experiment 5 applied Voronoi transformations to scenes to confirm that complexity of stimulus displays modulates the detection advantage for upright faces. These experiments demonstrate that context influences what can be learned about the face detection process. In complex visual arrays and natural scenes, detection proceeds more effectively when external facial features are preserved in an upright orientation. These findings are consistent with a cognitive detection template that focuses on general face-shape information.

Face-Selective Patches in Marmosets Are Involved in Dynamic and Static Facial Expression Processing.

The correct identification of facial expressions is critical for understanding the intention of others during social communication in the daily life of all primates. Here we used ultra-high-field fMRI at 9.4 T to investigate the neural network activated by facial expressions in awake New World common marmosets from both male and female sex, and to determine the effect of facial motions on this network. We further explored how the face-patch network is involved in the processing of facial expressions. Our results show that dynamic and static facial expressions activate face patches in temporal and frontal areas (O, PV, PD, MD, AD, and PL) as well as in the amygdala, with stronger responses for negative faces, also associated with an increase of the respiration rates of the monkey. Processing of dynamic facial expressions involves an extended network recruiting additional regions not known to be part of the face-processing network, suggesting that face motions may facilitate the recognition of facial expressions. We report for the first time in New World marmosets that the perception and identification of changeable facial expressions, vital for social communication, recruit face-selective brain patches also involved in face detection processing and are associated with an increase of arousal.SIGNIFICANCE STATEMENT Recent research in humans and nonhuman primates has highlighted the importance to correctly recognize and process facial expressions to understand others' emotions in social interactions. The current study focuses on the fMRI responses of emotional facial expressions in the common marmoset (Callithrix jacchus), a New World primate species sharing several similarities of social behavior with humans. Our results reveal that temporal and frontal face patches are involved in both basic face detection and facial expression processing. The specific recruitment of these patches for negative faces associated with an increase of the arousal level show that marmosets process facial expressions of their congener, vital for social communication.

An automated hyperparameter tuned deep learning model enabled facial emotion recognition for autonomous vehicle drivers

The progress of autonomous driving cars is a difficult movement that causes problems regarding safety, ethics, social acceptance, and cybersecurity. Currently, the automotive industry is utilizing these technologies to assist drivers with advanced driver assistance systems. This system helps different functions to careful driving and predict drivers' ability of stable driving behavior and road safety. A great number of researches have shown that the driver's emotion is the major factor that handles the emotions, resulting in serious vehicle collisions. As a result, continuous monitoring of drivers' behavior could assist to evaluate their behavior to prevent accidents. The study proposes a new Squirrel Search Optimization with Deep Learning Enabled Facial Emotion Recognition (SSO-DLFER) technique for Autonomous Vehicle Drivers. The proposed SSO-DLFER technique focuses mainly on the identification of driver facial emotions in the AVs. The proposed SSO-DLFER technique follows two major processes namely face detection and emotion recognition. The RetinaNet model is employed at the initial phase of the face detection process. For emotion recognition, the SSO-DLFER technique applied the Neural Architectural Search (NASNet) Large feature extractor with a gated recurrent unit (GRU) model as a classifier. For improving the emotion recognition performance, the SSO-based hyperparameter tuning procedure is performed. The simulation analysis of the SSO-DLFER technique is tested under benchmark datasets and the experimental outcome was investigated under various aspects. The comparative analysis reported the enhanced performance of the SSO-DLFER algorithm on recent approaches.

Weightless Neural Networks Face Recognition Learning Process for Binary Facial Pattern

The facial recognition process is normally used to verify and identify individuals, especially during the process of analyzing facial biometrics. The face detection algorithm automatically determines the presence or absence of a face. It is, however, theoretically difficult to analyze the face of a system with limited resources due to the complex pattern of a face. This implies an embedded platform scheme which is a combination of several learning methods supporting each other is required. Therefore, this research proposed the combination of the Haar Cascade method for the face detection process and the WNNs method for the learning process. The WNNs face recognition Algorithm (WNNs-FRA) uses facial data at the binary level and for binary recognition. Moreover, the sample face data in the binary were compared with the primary face data obtained from a particular camera or image. The parameters tested in this research include detection distance, detection coordinates, detection degree, memory requirement analysis, and the learning process. It is also important to note that the RAM node has 300 addresses divided into three face positions while the RAM discriminator has three addresses with codes (00), (10), and (10). Meanwhile, the largest amount of facial ROI data was found to be 900 pixels while the lowest is 400 pixels. The total RAM requirements were in the range of 32 , 768 bytes and 128 bytes and the execution time for each face position was predicted to be 33.3% which is an optimization because it is 66.67% faster than the entire learning process

Design of Automatic Room Door Lock System Based on Face Recognition

Face recognition is a method for recognizing human faces using a camera. There are various applications of facial recognition technology, one of which is Face Unlock technology on smartphones which functions as a security feature to open smartphone access through the user’s face. In this study, the use of facial recognition technology is used to automatically open the door of the room according to the registered face. The research method used is the waterfall method which has 5 stages. The research stages consist of requirements analysis, design, implementation & unit testing, integration & system testing, and operation & maintenance. This study uses a Raspberry Pi 4 to perform an automation system. The face detection process is based on the YuNet detection model, as well as the face recognition process using the SFace facial recognition model.

Intelligent facial expression recognition and classification using optimal deep transfer learning model

Facial expression is commonly utilized by humans to deliver their mood and emotional state to other people. Facial expression recognition (FER) becomes a hot research area in recent days, and it is a tedious process owing to the presence of high intra-class variation. The conventional methods for FEC are mainly based on handcrafted features with a classification model trained on image or video datasets. Since the facial datasets involve large variations in the images and comprise partial faces, it is needed to design automated FER models. The latest advancements in artificial intelligence (AI) and deep learning (DL) models find useful for better understanding of facial emotions related to face images. In this aspect, this paper presents an intelligent FER using optimal deep transfer learning (IFER-DTFL) model. The proposed IFER-DTFL technique aims to detect the face and identify the facial expressions automatically. The IFER-DTFL technique encompasses a three state process: face detection, feature extraction, and expression classification. In addition, a mask RCNN model is used for the detection of faces. Moreover, the Adam optimizer with Densely Connected Networks (DenseNet121) model is employed for feature extraction process. Furthermore, the weighted kernel extreme learning machine (WKELM) model is utilized to classify the facial expressions. A comprehensive set of simulations were carried out on benchmark dataset and the results are inspected under varying aspects. The experimental results pointed out the supremacy of the IFER-DTFL technique over the other recent techniques interms of several performance measures. • Propose a deep learning based facial expression recognition technique. • Apply Mask RCNN model for face detection process. • Employ Adam optimizer with DenseNet and WKELM model for recognition.

Detection of the Use of Mask to Prevent the Spread of COVID-19 Using SVM, Haar Cascade Classifier, and Robot Arm

In the effort to hold up the case spread of COVID-19’s growth rate by implementing health protocols such as the use of masks, supervision is needed especially for the people who have not or still have problems to wearing masks. In this research, the system utilizes the robotic power to identify visitors whether they are wearing masks or not, and automatically distribute masks if the user is detected as not wearing a mask. The user face detection process uses the Haar Cascade Classifier algorithm and SVM (Support Vector Machine) to classify users who wear masks or not. For the user who is detected as not wearing masks, myCobot-Pi with the support of suction pump will distribute masks to users. The use of myCobot-Pi as a raspberry pi based robotic arm allows the application of the system on devices that are minimal in terms of specifications and size. Through trials by taking 41 examples of detection cases, 29 cases were found that managed to detect the correct use of masks. In addition, in this study we use PP sheet plastic protector to replace the packaging of the mask because it can be carried by the suction pump properly.

Application Of The Viola Jones Algorithm On Face Detection

The face is the central organ for human expression, recognition, and communication. The face consists of four very important sense organs, namely the nose, eyes, ears, and tongue. The human brain has the privilege of recognizing someone based on their face. Face Detection is used to find out faces or nonfaces in an image and this section is the first step in the face detection process so its presence is very vital. Face detection task is very easy for humans, but this task is very complicated for computers because there are several stages that must be passed to detect faces. Where in this study the author designed and made an application for the application of the viola jones algorithm on face detection by discussing Python programming and using the Mysql database. The application of the viola jones algorithm on face detection goes well. Faces can be recognized by the application and display face owner information. Where the test is divided into 5 (five) including lighting testing with a test percentage level of 66.67% of detected faces, testing distance testing of 100% of faces detected at a distance of 1-100 cm, testing the number of faces there are 6 (six) results detected with 2 (two) faces, where faces must be approximately 10 cm apart so that faces can be detected and no results are detected on 3 (three) faces. So it can be concluded that the application cannot capture all faces detected by the camera. different face testing percentage test 100% the application can detect different people's faces with a single face.

Advance Security by Using Face Recognition

Abstract: In this paper we have proposed face recognition door lock system using raspberry pi for enhancing security purpose. Implementation of the device is for monitoring whether any unknown person is entering from the door. In order to get accurate and clear picture of an victim we have proposed Haar Cascade method for detecting face. As soon as the person enters near the door, pi camera captures the image of that person and face detection process is done then if it matches with images available in database then the door is unlocked otherwise a message with the picture of a person will be sent to the registered mobile through LAN network. The main motto of this project is to utilization of sensor with the combination of embedded equipment for the door opening systems. The main purpose of this system is to provide best security for home and office by using face recognition technique.

Students Attendances System Using Face Recognition

Abstract: Now a days we are living in this world where everything is automated and linked online. Internet are the things discover and it is used all over a world very beneficially.in human body face is the crucial factor for identifying each person. It can be identified by using different method like biometric for taking attendance. But in this method many more time are required to take attendance and also people are in contact with each other while marking their attendance in this pandemic situation we are introducing new technology student attendance system using face reorganization. Generally in a classroom the attendance was taken manually at ending or beginning of the class. The problem is that they required a lot of time to be taken and some manual and paper work will make a chance of mistake. To overcome from this problem we are introducing face recognition base attendance system. It is used in many application for identification of human face in a digital image or live video stream video. The proposed system make used of Haar classifier, KNN, CNN, SVM and global filters. After this recognition attendance report will be generated in excel format. The overall accuracy and complexity are calculated after testing this system it is cost efficient and need less installation time. Keywords: Face recognitions, Face detection, Haar classifier, CNN, KNN, SVM, LBPH, Automatic Attendances and image processing.

Face Recognition Based Unsupervised Attendance File Generation Using Local Binary Pattern Histogram

Attendance management system is an important part of daily online /offline classroom evaluation. At the beginning and end of class, it is usually checked by the teacher, but it may appear that a teacher may miss someone or some students answer multiple times. Our project is based on face recognition and face recognition technologies. The concept of face recognition is to give a computer system the ability to find and recognize human faces fast and precisely in images or videos. Computers that detect and recognize faces could be applied to a wide variety of practical applications including criminal identification, security systems, identity verification etc. The entire process involved in our project can be categorized into two main processes as face detection and face recognition process. Face detection involves the detection of an input image for further processing. Face Recognition, where the detected and processed face is compared to the database of known faces to decide the correct person. The attractiveness of the proposed system is, it generates an attendance file which includes the subject name alongside other parameters which are already present. Easy accessibility of attendance using excels technology.

Optimal feature selection with hybrid classification for automatic face shape classification using fitness sorted Grey wolf update

Depending on the prevailing researches, the classification of face shapes could be deployed for numerous applications. This paper intends to develop a new method for face shape classification using intelligent approaches. The presented method includes three stages namely, (i) Face Detection (ii) Pre-processing (iii) Feature extraction (iv) Classification. The face detection process is used for identifying the most significant objects in the face, probably eyes, nose, etc. which is done using the Viola-Jones algorithm. Moreover, the pre-processing stage includes the Histogram Equalization (HE) model for enhancing the contrast of the image. The classification of face shapes is performed by a hybrid classifier that links Convolutional Neural Network (CNN) and Neural Network (NN). For performing the CNN-based classification, the images are directly given as input. On the other hand, NN-based classification requires features as input. Hence, the pre-processed image is again subjected to the feature extraction process, where the features are extracted using the Active Appearance Model (AAM) and the Active Shape Model (ASM). For reducing the length of extracted features, the optimal feature selection process is adopted, which is done by improved Grey Wolf Optimization (GWO) algorithm. As the main contribution, the features, number of hidden neurons in the convolutional layer of CNN, and training of NN (weight update) is optimally chosen by improved GWO so-called as Fitness Sorted Grey Wolf Update (FS-GU) model. Finally, the average of two outcomes from both CNN, and NN provides the classified five categories of face shapes like heart, oblong, oval, round, and square. The performance of the proposed classification model is finally validated by comparing over the conventional models by analyzing the relevant performance metrics.

Implementasi Algoritma Haar Cascade pada Aplikasi Pengenalan Wajah

The large number of citizens in an organization makes the development of an attendance system or citizen detection in a place important in the running of work activities in the organization. Utilization of an IP Camera which is only used for regular monitoring without further detection of the needs of citizens in the organization made the development of personnel detection developed for monitoring the presence of personnel. With the development of a face detection system, it is hoped that the facial algorithm development system will be developed using an IP Camera. Face detection has been developed which has many and special features which aim to determine whether or not a face has been detected in an image. With image management that is developed in face detection, detection will be faster and more accurate because the color is processed into gray degrees so that there are fewer color pixels than those with colors. By using the Python programming language and an image detection library called OpenCV, less code will be designed. This study uses the Viola Jones method, which is a fast and accurate face detection method developed by Paul Viola and Michael Jones. In this study, the Viola Jones method uses the Haar Cascade algorithm which functions as a detection feature in the system and is combined with the internal image process and the AdaBoost Learning and Cascade Classifier so that the detected face object will easily classify whether the object is a face or not. In this case the Cascade Classfier used in this study is the face and eyes. The development of this algorithm is carried out for face detection and recognition. The detection is done by taking pictures with the process taken using a webcam. The system will take several pictures and then the image data will be stored in a folder called dataSet. After that, all data is trained so that it can be recognized by the system. With retrieval, detection and recognition limitations that can only be taken from a distance of less than three meters, face detection on the IP Camera can still read objects other than faces. With recognition and accuracy on the webcam camera, about 80,5% this system can be developed with the Haar Cascade algorithm and the Haar Cascade algorithm precisely to be applied to the development of faced detection and face recognition. By developing the Haar Cascade algorithm for face detection, problems and utilization of an organization's data can be more easily detected and used by IP cameras that can support the performance process of face detection and recognition

Face Recognition System using Micro Unmanned Aerial Vehicle

With the price of UAV or people often call Drone getting cheaper, making this technology can be enjoyed not only for limited communities but also for the general public. The camera features mounted on the drone can be used for face detection and recognition. In this paper, we implement Haar-Cascade based classifier for face detection and recognition using OpenCV Python on a micro UAV that only 34.7 gr weight. The face detection process is done in real-time using an FPV camera that is controlled indoors at a distance of less than 50m. From the results of the experiment, it appears that the angle of the face is very influential, especially on the face that is not fully open, like a woman who wears a hijab. The best angle to take is in the position of flying in front of the face in accordance with the slope of the FPV camera mounted on the micro UAV.

Comparison of Paul Viola – Michael Jones algorithm and HOG algorithm for Face Detection

The computer technology which helps to find the face of a person in video and image is face detection. It is like a biometric application that finds a person by analyzing and comparing the person’s face patterns. The face detection technique helps for security purposes, and also it has an rising interest in many fields. This technology has received good attention. It has some good applications based on law enforcement. For detecting face in an digital image, we have lot of algorithms. In this, Viola Jones algorithm and histogram of oriented gradients is used to detect the face of a person. The paper is proposed for a comparison between viola jones and Hog. By analyzing this we can find the gender, age and also emotions of the person. Here different type of images were used for detection. The Matlab tool and Jupyter is used for implementing this face detection process.

Visitor/Intruder Monitoring System using Haar-cascade Classifier Algorithm

Home security system plays a predominant role in the modern era. The purpose of the security systems is to protect the members of the family from intruders. The main idea behind this system is to provide security for residential areas. In today’s world securing our home takes a major role in the society. Surveillance from home to huge industries, plays a significant role in the fulfilment of our security. There are many machine learning algorithms for home security system but Haar-cascade classifier algorithm gives a better result when compared with other machine learning algorithm This system implements a face recognition and face detection using Haar-cascade classifier algorithm, OpenCV libraries are used for training and testing of the face detection process. In future, face recognition will be everywhere in the world. Face recognition is creating a magic in every field with its advanced technology. Visitor/Intruder monitoring system using Machine Learning is used to monitor the person and find whether the person is a known or unknown person from the captured picture. Here LBPH (Local Binary Pattern Histogram) Face Recognizer is used. After capturing the image, it is compared with the available dataset then their respective name and picture is sent to the specified email to alert the owner.