Fisher Face Research Articles

The Facial Recognition Technology in Academic Attendance: A Comparative Study for Real-Time Management

In today’s academic settings, managing daily tasks like attendance tracking has become increasingly burdensome. Traditional methods of manual attendance taking are inefficient and time-consuming, particularly with growing numbers of students and staff. To address this challenge, various approaches, including face identification systems, have been developed. This research introduces a smart face recognition (FR) system for managing attendance efficiently. The system employs multiple face recognition methodologies such as Local Binary Histogram, PCA/Eigen Face Recognizer, and Fisher Face Recognizer, enhancing system performance. These methods are combined using Ensemble Fusion to improve accuracy. Additionally, the system utilizes Multitask Cascaded Convolutional Network for face detection and attribute extraction. Extracted attributes are matched with stored facial templates to identify recognized faces and mark attendance. Integration with Cloud API facilitates record-keeping. The system also includes a feedback and notification system for process status indication. Results indicate that the proposed system achieves 82.1% accuracy in face recognition and requires minimal time (0.000081s) to predict and mark attendance.

INTRUDER DETECTION SYSTEM USING MACHINE LEARNING

The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisher face method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is MATLAB software. Keywords: Digital Image Processing, Face Detection, Face Recognition, Motion Detection

Research On Real Time Emotion Recognition Using Digital Image Processing Using ML

Face recognition technology has garnered significant attention in recent years due to its wide range of applications in various fields such as security, surveillance, biometrics, and human-computer interaction. This abstract provides a comprehensive overview of the advancements in face recognition image processing techniques, methodologies, and applications. The abstract begins by elucidating the fundamental concepts underlying face recognition, including feature extraction, dimensionality reduction, and classification algorithms. Various approaches such as Eigenfaces, Fisher faces, and Local Binary Patterns (LBP) are discussed, highlighting their strengths and limitations. Moreover, recent developments in deep learning techniques, particularly convolutional neural networks (CNNs) and Siamese networks, have revolutionized face recognition by achieving remarkable accuracy and robustness. The abstract delves into the architecture and training procedures of these deep learning models, emphasizing their ability to learn discriminative features directly from raw pixel data. Furthermore, the abstract explores the challenges faced by face recognition systems, such as variations in pose, illumination, expression, and occlusion. Techniques for addressing these challenges, including data augmentation, normalization, and adversarial training, are examined.

Facial Recognition Attendance Monitoring System using Deep Learning Techniques

The Facial Recognition Attendance Monitoring System employing Deep Learning Techniques represents a cutting-edge application of artificial intelligence in educational and corporate environments. The implementation of a Facial Recognition System can aid in identifying or verifying a person's identity from a digital image. Accurate attendance records are vital to classroom evaluation. However, manual attendance tracking can result in errors, missed students, or duplicate entries. The adoption of the Face Recognition-based attendance system could help eliminate these shortcomings. This innovative approach involves utilizing a camera to capture input images, detecting faces using algorithms such as Haarcascade, Eigen values, support vector machines, or the Fisher face algorithm, verifying the faces against a database of student profiles, and marking attendance in an Excel sheet. The use of OpenCV, an open-source computer vision library, ensures the efficient

Facial Recognition Attendance Monitoring System using Deep Learning Techniques

The Facial Recognition Attendance Monitoring System employing Deep Learning Techniques represents a cutting-edge application of artificial intelligence in educational and corporate environments. The implementation of a Facial Recognition System can aid in identifying or verifying a person's identity from a digital image. Accurate attendance records are vital to classroom evaluation. However, manual attendance tracking can result in errors, missed students, or duplicate entries. The adoption of the Face Recognition-based attendance system could help eliminate these shortcomings. This innovative approach involves utilizing a camera to capture input images, detecting faces using algorithms such as Haarcascade, Eigen values, support vector machines, or the Fisher face algorithm, verifying the faces against a database of student profiles, and marking attendance in an Excel sheet. The use of OpenCV, an open-source computer vision library, ensures the efficient functioning of the system.

FaceBreach Alert: Multi-Modal Intrusion Detection emphasizing Face Recognition

Abstract: The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognize a face as individuals. Stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the muchstudied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition pattern namely, Eigenface method and Fisher face method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigen faces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project face detection system with face recognition is Image processing. The software requirements for this project is MATLAB software.

Face Recognition Based Attendance System

Abstract: If attendance is managed manually, it can be quite taxing on the teachers. Smart and automated attendance management systems are being used to address this issue. This architecture makes it simple to address the issue of proxies and students being counted as present even though they are not. The system implemented makes use of the LBPH face recognizer to instantly recognise a person's face. Light has an impact on both Eigen and Fisher faces, and real-world lighting conditions cannot always be ideal. This issue can be solved by a change in the LBPH faces. This method decides who is present and who is not by comparing the images from the test and training sessions. A system-updated excel sheet that contains the attendance information is kept in the database. The faculty can get a copy of the attendance sheet through mail by clicking auto mail button in the GUI. The face recognizer detects faces using Haar Cascade Classifier, and recognizes the faces using Local Binary Patterns Histograms (LBPH) algorithm.

Security Technologies Using Facial Recognition

Abstract
 
 Faces are one of the simplest methods to determine a person's identity. Face recognition is a unique identifying method that uses an individual's traits to determine the identity of that individual. The proposed recognition process is divided into two stages: face recognition and object recognition. Unless the item is very close, this procedure is very rapid for humans. The recognition of human faces is introduced next. The stage is then reproduced and used as a model for facial image recognition (face recognition). That's one of the professionally created and well-researched biometrics procedures. The eigenface approach and the Fisher face method are two common face recognition pattern algorithms that have been developed. Recognition of facial images The Eigenface approach is based on the reduction of face dimensional space for facial traits using Principal Component Analysis (PCA). The major goal of applying PCA on face recognition was to generate Eigen faces (face space) by identifying the eigenvector corresponding to the face image's biggest eigenvalue. Image processing and security systems are areas of interest in this research face recognition integrated into a security system.
 
 Keywords: face recognition, security systems, camera, python;

Design of Boosted Cascade and RTLBP Features based Face Recognition Module Using ML in OpenCV

Over the past ten years, face recognition in images has become more and more important. Since facial recognition is a natural talent for humans, it could be challenging to train a computer to recognize faces. The detecting difficulty comprises three stages. Without the requirement for picture difference or skin color identification, the detector operates in real-time at fifteen frames per second. Proposed and developed is a face recognition system based on Real Time Local Binary Pattern (RTLBP) and Boosted Cascade of Simple Features (BCSF). Preprocessing, face identification and recognition, and annotation are the three stages of the system. Segmentation, filtering, thresholding, and improving image quality are all part of preprocessing. The face, which is crucial for identification, is identified using the BCSF approach. Every face in the database has a correlation and RTLBP calculation. Correlation and RTLBP analysis between the captured face and the database face is then used to conduct recognition. One statistical technique that falls within the large category of factor analysis is the Boosted Cascade of Simple Features. By reducing the massive quantity of data stored to the extent of the feature space needed to represent the data efficiently, the BCSF seeks to achieve this goal. The RTLBP is designed to recognize faces by using a wide 1-D pixel vector consisting of a 2-D face picture in compact main parts of the space function. This is referred to as self-space projection. The identity of the vectors in the covariance matrix, which is centred on a set of fingerprint images, helps determine the proper space. Using OpenCV, Open Computer Vision (OpenCV), Python, Eigen-face, Fisher Face, LBPH, and Haar Cascade programming, an algorithm for a camera-based, real-time face recognition system is developed in this project. The name of the detected individual appears in the top left corner of a highlighted rectangle that annotates the face. The process of recognition takes 10.18 seconds, with a 99.93% success rate.

Face Recognition for Examinee Verification

In the modern world, facial recognition is playing a vital role in the field of biometric technologies. The reason being simple, it’s a very efficient and developed method compared to the other methods. Its being so precise, errorless and effective gives it an edge over other technologies. There are lot of fields where this fast growing technology is yet to show its effectiveness, one of which is examinations, the identification of the students during examinations. Different kinds of biometric technologies are used in the examination sector in order to identify the students appearing for the exams. Biometric technologies use physical features to identify the person appearing in the exam but many of these traditional methods create room for errors and cheating which can be improved by execution of facial technology.In this research, the approach of Eigenface and fisherface has been used. These techniques are recent and have apparently promising performances, and are representing new trends in this field. Based on previous research that has been done by other researchers about the Eigen face and Fisher face algorithms, where facial image recognition uses Eigenface with different conditions of differentiation from expression, with success rates up to 75%. Ada-boost face recognition, Eigen face PCA and MySQL produce 80% of various different conditions. Face recognition with 93% success with Fisher face by 73 face trials of different expressions and different positions. From previous research, the author wanted to know which algorithm has speed in terms of time, distance and accuracy of photosensitivity in the facial recognition process. By tests of the two algorithms, it produces success percentages and accuracy charts. The Fisherface algorithm is faster than the Eigenface algorithm has an accuracy of 96% and the Fisherface algorithm has an accuracy of 97%.

Deep Learning for Forgery Face Detection Using Fuzzy Fisher Capsule Dual Graph

In digital manipulation, creating fake images/videos or swapping face images/videos with another person is done by using a deep learning algorithm is termed deep fake. Fake pornography is a harmful one because of the inclusion of fake content in the hoaxes, fake news, and fraud things in the financial. The Deep Learning technique is an effective tool in the detection of deep fake images or videos. With the advancement of Generative adversarial networks (GAN) in the deep learning techniques, deep fake has become an essential one in the social media platform. This may threaten the public, therefore detection of deep fake images/videos is needed. For detecting the forged images/videos, many research works have been done and those methods are inefficient in the detection of new threats or newly created forgery images or videos, and also consumption time is high. Therefore, this paper focused on the detection of different types of fake images or videos using Fuzzy Fisher face with Capsule dual graph (FFF-CDG). The data set used in this work is FFHQ, 100K-Faces DFFD, VGG-Face2, and Wild Deep fake. The accuracy for FFHQ datasets, the existing and proposed systems obtained the accuracy of 81.5%, 89.32%, 91.35%, and 95.82% respectively.

Face and Liveliness Detection Based Smart Bank Locker

Face is one of the easiest ways to differentiate each other's identities. Facial recognition is a personal identification system that uses the personal characteristics of someone to identify who you are. The process of recognizing a person's face is basically it consists of two stages, namely facial recognition, in which this process occurs most frequently immediately to humans, except under circumstances where the object is available in a short period of time far, next is an introduction, which recognizes faces as individuals. Stage then it is repeated and developed as a model for face image recognition (face recognition) is one of the most widely studied biometric technologies and developed by experts. There are two types of currently popular methods of advanced facial recognition pattern namely, Eigen face method and Fisher face method. We use fisher face a monitoring method to verify any system. The location of this project is facing image processing system. The software requirements for this project are an anaconda.

Face recognition methods analysis

Face Recognition is one of the most important issues in Image processing tasks. It is important because it uses for various purposes in real world such as Criminal detection or for detecting fraud in passport and visa check in airports. Face book is a nice example of Face recognition application, when it sends notification to one user’s friends who are recognized by their images that user uploaded in face book page. To solve Face Recognition problem different methods are introduced such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Hidden Markov Models (HMM) which are explained and analyzed. Also algorithms like; Eigen face, Fisher face and Local Binary Pattern Histogram (LBPH) which are simplest and most accurate methods are implemented in this project for AT&T dataset to recognize the most similar face to other faces in this data set. To this end these algorithms are explained and advantages and disadvantages of each one are analyzed as well. Consequently, the best method is selected with comparison between the results of face reconstruction by Engine face, Fisher face and Local binary pattern histogram methods. In this project Eigen face method has best result. It should be noted that for implementing face recognition algorithms color map methods are used to distinguish the facial features more precisely. In this work Rainbow color map in Eigen Face algorithm and HSV color map in Fisher Face algorithm are utilized and results shows that HSV color map is more accurate than rainbow color map.

Detection of Face Emotion and Music Recommendation System using Machine Learning

Face emotion detection has recently attracted a lot of interest because of its uses in computer vision and the field of human-computer interaction. Various methods and applications were suggested and put into use as a result of the ongoing research in this area. In this study, we present an emotion-recognition recommender system that can identify a user’s feelings and offer a selection of suitable songs that might lift his spirits. To gather information and enable us to give the users a selection of music tracks that are effective at lifting the users’ spirits, a quick search was undertaken to learn how music may impact the user mood in the short term. The suggested system recognizes emotions, and if the individual is feeling down, a special playlist including the best kinds of music will be played to lift his spirits. On the other hand, if a favorable mood is recognized, an appropriate playlist will be offered that contains several genres of music that will amplify the pleasant feelings. Principal Component Analysis (PCA) methods and the Fisher Face algorithm are used to implement the suggested recommender system.

Facial Recognition in Public Areas

The security of information nowadays is very significant and difficult, so there are a number of ways to improve security. Especially in public areas like airports, railway stations, Universities, ATMs, etc. and security cameras are presently common in these areas. So, in this paper, we are presenting how Facial recognition can be used in public areas like airports, toll gates, offices, etc. We are comparing or matching a face of a person who we want to detect, with the video which is recorded through CCTV. There are certain algorithms to detect faces from video like through HAAR cascades, eigenface, fisher face, etc. open-source computer vision library is used for facial recognition.

Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions

Facial recognition (FR) in unconstrained weather is still challenging and surprisingly ignored by many researchers and practitioners over the past few decades. Therefore, this paper aims to evaluate the performance of three existing popular facial recognition methods considering different weather conditions. As a result, a new face dataset (Lamar University database (LUDB)) was developed that contains face images captured under various weather conditions such as foggy, cloudy, rainy, and sunny. Three very popular FR methods—Eigenface (EF), Fisherface (FF), and Local binary pattern histogram (LBPH)—were evaluated considering two other face datasets, AT&T and 5_Celebrity, along with LUDB in term of accuracy, precision, recall, and F1 score with 95% confidence interval (CI). Computational results show a significant difference among the three FR techniques in terms of overall time complexity and accuracy. LBPH outperforms the other two FR algorithms on both LUDB and 5_Celebrity datasets by achieving 40% and 95% accuracy, respectively. On the other hand, with minimum execution time of 1.37, 1.37, and 1.44 s per image on AT&T,5_Celebrity, and LUDB, respectively, Fisherface achieved the best result.

A FACIAL RECOGNITION USING OPEN COMPUTER VISION

The Face recognition and image or video recognition are popular research topics in biometric technology. Real-time face recognition is an exciting field and a rapidly evolving issue. Key component analysis (PCA) may be a statistical technique collectively called correlational analysis . The goal of PCA is to scale back the massive amount of knowledge storage to the dimensions of the functional space required to render the face recognition system. The wide one-dimensional pixel vector generated from the two-dimensional image of the face and therefore the basic elements of the spatial function are designed for face recognition using PCA. this is often the projection of your own space. Sufficient space is decided by the brand. specialise in the eigenvectors of the covariance matrix of the fingerprint image collection. i'm building a camera-based real-time face recognition system and installing an algorithm. Use OpenCV, Haar Cascade, Eigen face, Fisher Face, LBPH and Python for program development.

Intelligent Music Player Based on Emotions

This project presents a system to automatically detect emotional dichotomy and mixed emotional experience using a Linux based system. Facial expressions, head movements and facial gestures were captured from pictorial input in order to create attributes such as distance, coordinates and movement of tracked points. Web camera is used to extract spectral attributes. Features are calculated using Fisher face algorithm. Emotion detected by cascade classifier and feature level fusion was used to create a combined feature vector. Live actions of user are to be used for recording emotions. As per calculated result system will play songs and display books list.

A multi-scale and rotation-invariant phase pattern (MRIPP) and a stack of restricted Boltzmann machine (RBM) with preprocessing for facial expression classification

In facial expression recognition applications, the classification accuracy decreases because of the blur, illumination and localization problems in images. Therefore, a robust emotion recognition technique is needed. In this work, a Multi-scale and Rotation-Invariant Phase Pattern (MRIPP) is proposed. The MRIPP extracts the features from facial images, and the extracted patterns are blur-insensitive, rotation-invariant and robust. The performance of classification algorithms like Fisher faces, Support Vector Machine (SVM), Extreme Learning Machine (ELM), Convolutional Neural Network (CNN) and Deep Neural Network (DNN) are analyzed. In order to reduce the time for classification, an OPTICS-based pre-processing of the features is proposed that creates a non-redundant and compressed training set to classify the test set. Ten-fold cross validation is used in experimental analysis and the performance metric classification accuracy is used. The proposed approach has been evaluated with six datasets Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK +), Multi- media Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and Man–Machine Interaction (MMI) datasets to meet a classification accuracy of 98.2%, 97.5%, 95.6%, 35.5%, 87.7% and 82.4% for seven class emotion detection using a stack of Restricted Boltzmann Machines(RBM), which is high when compared to other latest methods.

Face Detection and Recognition using Open CV Based on Fisher Faces Algorithm

Facial Recognition represents the event of a system which may determine the person with the assistance of a face using Computer Vision (Open CV). Face recognition is employed within the fields of Identity Recognition, police investigation and enforcement. It's a method of characteristic someone supported facial expression. This method is enforced in 2 stages. They're the training stage and therefore the testing stage. This study primarily consists of 3 elements, specifically face detection from the image, feature extraction and storing many reminder images, and recognition. Face finding rule is employed to detect the face from the given image. The foremost helpful and distinctive options of the face image are extracted within the feature extraction part. Face Detection may be challenging because of pictures and video frames will contain advanced background, completely different head poses and occlusion like carrying glasses or scarf. It presents a rule for finding face recognition downside and concatenated into one feature vector that is employed to coach the system to recognise among the prevailing photos with it. Within the testing stage the system takes the face of the image of someone for recognition. Image acquisition, pre-processing, image filtering, feature extraction is just like the learning stage. For classification the options are fed to the trained system. The algorithms can determine the face image from the content and acknowledges it.