FACE RECOGNITION USING EIGENFACE APPROACH

… Journal of Recent …, 2009

Abstract—Face recognition systems have been grabbing high attention from commercial market point of view as well as pattern recognition field. It also stands high in researchers community. Face recognition have been fast growing, challenging and interesting area in ...

1st Conference of Industrial Technology ( CIT2017), 2018

Recognizing different faces at different times is challenging subject but at the same time it gives rewarding in various areas in society. The police force in solving crime, academic institutes to know students available for lectures, monitoring the in and out movement of people and many more. This paper presents a methodology for face recognition based on Principal component analysis (PCA) techniques. The study aims to implement a model for a particular face and distinguish it from a large number of stored faces. Euclidian distance was used for classification of test images. Reconstruction process simply implied as reversing the eigenface procedure. The proposed method was tested on Olivetti and Oracle Research Laboratory (ORL) face database. Some of such studies gave a result of recognition rate of 96%. There are more others ways of studies that carried out for such study.

Access control systems are used to grant or deny the access to a person of a particular resource. There has been an enormous change in the trend of access control systems in recent times. Starting with the use of physical access control systems such as tokens, passwords etc., for the identification of a person, the trend has swayed towards designing and deploying of access control systems which use biometric identification of individual persons, for the grant or denial of access to resources. Biometric identification methods use various sources like retina, fingerprint, DNA etc., Biometric sources can be classified into two, namely physiological and behavioral. The former includes face, fingerprint, hand, iris, DNA and the latter includes keystroke, signature and voice. The access control systems using these biometric sources fundamentally identify and recognize a particular personal trait of person and compare it with the information available to grant or deny access to such person who seeks to interact with such system. As amongst such biometric sources to develop reliable access control systems researches have shown overt interest in using the face of a person (face recognition). Such inclination of the researchers is due to the various strategic advantages face recognition systems have like, its global application, wide and compatible collectability of data, cost effectiveness in implementation (for example existing surveillance cameras can be used to deploy such systems) when compared to other biometric methods and many more. The current project fundamentally aims at successfully designing and implementing a face recognition technology to develop an access control system. Out of the various available methods for developing a face recognition technology such as Fishersface, Hidden Markov model, dynamic link matching, three-dimensional face recognition, Eigenfaces etc., this project adopts Eigenfaces method of face recognition to achieve such aim. The project fundamentally detects and identifies human faces that work as a biometric source. This project aims to provide solution for the development of face recognition system by assuming that, problems involved in developing such systems are intrinsically a two dimensional rather than a three dimensional. It basically identifies the face of a person in a face image and then identifies the specific characteristics of such face image, then compares such characteristics with an existing database containing specific characteristics of several faces of different individuals, to decide if the former matches with any of the existing faces in the database. Eigenfaces method is utilised to achieve the above result where, face image is projected onto a feature space that spans the significant variations among known faces. The best variation among different images is calculated where during such calculation not exactly the facial features like eyes, nose, ears, etc., are classified. Instead it learns each face in an under constant observation as a whole.

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.

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

Face recognition is an important and challenging field in computer vision. This research present a system that is able to recognize a person's face by comparing facial structure to that of a known person which is achieved by using frontal view facing photographs of individuals to render a two-dimensional representation of a human head. Various symmetrization techniques are used for preprocessing the image in order to handle bad illumination and face alignment problem. We used Eigenface approach for face recognition. Eigenfaces are eigenvectors of covariance matrix, representing given image space. Any new face image can then be represented as a linear combination of these Eigenfaces. This makes it easier to match any two given images and thus face recognition process. The implemented eigenface-based technique classified the faces 95% correctly.

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.

iaeme

Face recognition have been fast growing, challenging and interesting area in real-time applications. A large number of face recognition algorithms have been developed from decades. The present paper primarily focuses on principal component analysis, for the analysis, the software is implemented using Matlab and C#.net This face recognition system detects the faces in a picture taken by web-cam, and these face images are then checked with training image dataset based on Eigen features. Eigen features are used to characterize images.

2012

In this paper an approach to the detection and identification of human faces is presented and then recognizes the person by comparing characteristics of the face to those of known individuals is described. A face recognition system using the Principal Component Analysis (PCA) algorithm was implemented. The algorithm is based on an eigen faces approach which represents a PCA method in which a small set of significant features are used to describe the variation between face images. Experimental results for different numbers of eigen faces are shown to verify the viability of the proposed method.