Mathematical Modeling for Face Recognition system

Journal of Wasit for Science and Medicine, 2023

This paper describe the principle of eigenface approach to detection and identification human face. In this approach, the features extracted from face images are individual, hidden, light insensitive, and effective to recognition. Wherever approach treats the face recognition as two dimensions recognition problems, taking advantage of the fact that faces are normally upright and thus may be described by a small set of two characteristics view. The face images for training and testing are selected carefully from Olivetti and Oracle Research Laboratory (ORL) face database in order to have minmum pose variation. The eigenface approach is proposed seems to be an adequate method to be used in face recognition due to its simplicity, speed, and learning capability. Face recognition plays an important role in biometrics base personal identification . Abiometrics is, "Automated methods of recognizing an individual based on their unique physical or behavioral characteristics." The process of facial recognition involves automated methods to determine identity, using facial features as essential elements of distinction . The important fact which is considered is that although these face images have high dimensionality, in reality

2013

Face recognition security systems have become important for many applications such as automatic access control and video capturing . Most of the face recognition Eigenspace systems today require proper frontal views of a person, and these systems will fail if the person to be recognized does not face the camera correctly. In this paper, we find the face capturing and storing the database . Our recognition system is insensitive to viewing directions and it requires only one sample view per person. The proposed approach utilizes the similarities of a face image against a set of faces from a training set at the same view to establish style and recognition invariant representations of a person in different poses. In this paper we store the no of face recognition through Eigenspace-based . In this paper to store the image database and match proper face .

International Journal of Scientific & Technology Research, 2020

In this paper, a methodology for face recognition using Eigen faces is being discussed. The key idea of the proposal we consider a set of images then we applied Eigen faces on that set for recognition of faces. First a set of images has been considered as a training set. Then calculations were performed on training set, finally the images have been concluded by computing the Euclidian distance between the image in the training set and newly computed image. Test results for various number of faces are appeared to confirm the suitability of the proposed technique and also we discussed some image metrics. Finally we demonstrated the security analysis like time complexity.

International Journal of Image, Graphics and Signal Processing, 2019

Real-time face image recognition is a face recognition system that is done directly using a webcam camera from a computer. Face recognition system aims to implement a biometrics system as a real-time facial recognition system. This system is divided into two important processes, namely the training process and the identification process. The registration process is a process where a user registered their name in a system and then registers their face. Face data that has been registered will be used for the next process, namely the identification process. The face registration process uses face detection using the OpenCV library. The feature extraction process and introduction to the recognition system use the Eigenface method. The results of this study found that, the Eigenface method is able to detect faces accurately up to 4 people simultaneously. The greater the threshold value will result in a greater value of FRR, while there isn't any FAR value found from different thresholds. The level of lighting, poses, and facial distance from the camera when training and testing the face image heavily influences the use of the eigenface method.

2007

Face recognition has rapidly evolved and has become very popular in recent years. It is being intensively researched and found many applications, primarily in biometric security systems. One of the main challenges in face recognition is to identify different features playing fundamental role in face description. The role of color, which appears to be a salient attribute of faces, is still debatable within the literature. Some research has suggested that it confers little recognition advantage for identifying faces while other research suggests that color is a very important cue. The clear perception of colors in the environment illustrates that color must be important in the interpretation of complex scenes and recognizing objects in the environment. In this paper several face recognition algorithms are reviewed compared using both grayscale and color images. The accuracy of each algorithm has been determined and they were ranked according to recognition rates. A system is also proposed, which uses color features for face recognition. This system can be used by different face recognition algorithms. The experiments have been performed on two wimagesell-known databases: CVL database containing 114 individuals with 7 images per individual and Georgia face database consisting of 50 individuals with 15 pictures of each individual taken with different poses, expressions and backgrounds.

International Journal of Computer Applications, 2015

We tried to develop a real time face detection and recognition system which uses an “appearance-based” approach. For detection purpose we used Viola Jones algorithm. To recognize face we worked with Eigen Faces which is a PCA based algorithm. In a real time to recognize a face we need a data training set. For data training set we took five images of each person and manipulated the Eigen values to match the known individual.

gunadarma.ac.id

There is a need for the automatic monitoring of an environment for surveillance purposes, attendance management and access control. This automatic monitoring system becomes very meaningful if coupled with the ability to identify persons present in such environment. Most surveillance and monitoring systems are basically video cameras or CCTVs which give data in form of videos and images. The process of identification of persons in the videos or images can be taken care of through the detection and recognition of faces present in the images. This project states the design of a face detection and recognition system implemented with MATLAB. Viola Jones algorithm was employed for the detection of faces. The system based on Viola Jones algorithm was slow during implementation, hence this research work attempts to enhance the speed of detection. Principal Component Analysis (PCA) was employed for dimensionality reduction, and the matching correlation analysis for matching. The performance of the system was measured based on the detection speed and recognition efficiency, the system was tested with 88 test images. Case studies were taken for the performance of the system with varying skin color and with occlusion.

Face recognition is the technique which can be applied to the wide variety of problems like image and film processing, human computer interaction, criminal identification etc. This has motivated researchers to develop computational models to identify the faces, which are easy and simple to implement. In this, demonstrates the face recognition system in android device using eigenfaces. The implemented system is able to perform real-time face detection, face recognition and can give feedback giving a window with the subject's info from database and sending an e-mail notification to interested institutions using android application.

International Journal of Engineering Sciences & Research Technology, 2013

This paper discuss the problem of Face is a complex model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is Feature extraction using Principle, Component Analysis and recognition using the feed forward back propagation Neural Network. The goal is to implement the system (model) for a particular face and distinguish it from a large number of stored faces with some real-Analysis (PCA) algorithm for the recognition of the images. It gives us efficient way to find the lower dimensional space. Abstract This paper discuss the problem of Face is a complex multidimensional visual model and developing a computational model for face recognition is difficult. The paper presents a methodology for face recognition based on information theory approach of coding and decoding the face image. Proposed methodology is connection of two stages Feature extraction using Principle, Component Analysis and recognition using the feed forward back propagation Neural Network. The goal is to implement the system (model) for a particular face and distinguish it from a large -time variations as well. The Eigen face approach uses Principal Component Analysis (PCA) algorithm for the recognition of the images. It gives us efficient way to find the lower dimensional on; Principal Component Analysis; Eigen faces; Eigen values; Eigenvector.