Face Recognition Using Eigen Face Based Technique Utilizing the Concept of Principal Component Analysis (original) (raw)
Related papers
2012
Face recognition using eigen faces is an approach to the detection and identification of human faces and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space `face space' that best encodes the variation among known face images. The face space is defined by the `eigen faces', which are the eigenvectors of the set of faces. They do not necessarily correspond to isolated features such as eyes, ears, and noses. Eigen faces are obtained from eigenvectors of an image which is a principle component of analysis. The principal component analysis (PCA) is one of the most successful techniques that have been used in image recognition and compression. The main idea of using P...
Human Face Recognition based on Principal Component Analysis and EigenFaces
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.
Facial recognition using eigenfaces by PCA
… Journal of Recent …, 2009
AbstractFace 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 ...
Face Recognition using Eigenvector and Principle Component Analysis
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.
Eigen Faces and Principle Component Analysis for Face Recognition Systems: A Comparative Study
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
Face recognition has been largely used in biometric field as a security measure at air ports, passport verification, criminals' list verification, visa processing, and so on. Various literature studies suggested different approaches for face recognition systems and most of these studies have limitations with low performance rates. Eigenfaces and principle component analysis (PCA) can be considered as most important face recognition approaches in the literature. There is a need to develop algorithms and approaches that overcome these disadvantages and improve performance of face recognition systems. At the same time, there is a lack of literature studies which are related to face recognition systems based on EigenFaces and PCA. Therefore, this work includes a comparative study of literature researches related to Eigenfaces and PCA for face recognition systems. The main steps, strengths and limitations of each study will be discussed. Many recommendations were suggested in this st...
COMPARATIVE ANALYSIS OF FACE RECOGNITION BASED ON SIGNIFICANT PRINCIPAL COMPONENTS OF PCA TECHNIQUE
IAEME, 2019
Face recognition systems have been emerging as acceptable approaches for human authorization. Face recognition help in searching and classifying a face database and at a higher level help in identification of possible threats to security. In face recognition problem, the objective is to search a face in the reference face database that matches a given subject. The task of face recognition involves the extraction of feature vectors of the human face from the face image for differentiating it from other persons [6]. In this work, the comparative analysis is done based on the varying number of highly significant principal components (Eigenvectors) of PCA for face recognition. Experimental results show a small number of principal components of PCA are required for matching. PCA technique is a statistical technique, it reduces the dimension of the search space that best describes the images.
Face Recognition System Using Eigenface Method based
Development of face recognition methods afford capable method to recognizing face with expression, pose, noise variation and changes caused by illumination effects. In kind among others is eigenface method based on the Karhunen-Loeve expansion in pattern recognition, in other words the principal component analysis (PCA). Eigenface method reduce the N-dimension of face images to feature space M-dimension. Vector whole face images and facial component region saved on database. there are 6 component region that is forehead, eyes, nose, mouth, left side and right side. Face division is conducted when image saved on database and face recognition process. Each region is used to input eigenface process to get its feature space. Testing by considering characteristic of each facial component region gives better contribution than reference to characteristic whole face only. Even eigenface method gives succesfull recognition rate with adds effect as noise speckle, motion and illumination.
DESIGN OF FACE RECOGNITION SYSTEM USING PRINCIPAL COMPONENT ANALYSIS
Face is considered to be one of the most important visual objects for identification. Recognition of human face is complex and it converts the face into a mathematical model. Face recognition is the most efficient and sophisticated method for the security systems. It is a biometric technology with a wide range of applications such as use in ATM machines, preventing voter's fraud, criminal identification, human computer interaction, etc. This paper describes the building of a face recognition system by using Principal Component Analysis method. PCA is the method for reduce the data dimension of the image. It is based on the approach that breaks the face images into a small set of characteristic feature images. These"eigenfaces" are the principal components of the initial data set of face images. Recognition is done by comparing the input face image with the faces in the data set through distance measuring methods. Here the face recognition system is developed using Matlab and it recognizes the input face from a set of training faces.
IJERT-A Novel approach for Face Recognition System Based on Eigen Vector
International Journal of Engineering Research and Technology (IJERT), 2014
https://www.ijert.org/a-novel-approach-for-face-recognition-system-based-on-eigen-vector https://www.ijert.org/research/a-novel-approach-for-face-recognition-system-based-on-eigen-vector-IJERTV3IS080668.pdf Face Recognition is one of the most important requirement in recent years for security as well biometric and other applications. Face recognition is a computer based approach and accuracy is the most challenging task for researchers and programmers. Linear Discriminate Analysis, Elastic Bunch Graph Matching using the Fisher face algorithm, the Hidden Markov model, the Multilinear Subspace Learning using tensor representation, and the neuronal motivated dynamic link matching are also the techniques which are available for face recognition. But no one technique we can say robust as each has a some limitations like much complex algorithm or higher processing time etc. This paper presents a new approach which provides robustness for accurate face detection. This technique for face recognition is called principal component analysis based technique. It follows the sequence of finding hermitian matrix, Eigen vector, Eigen face matrix and Euclidean distance methodology.