Human Face Recognition based on Principal Component Analysis and EigenFaces (original) (raw)
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INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
Face recognition has been an active research area since late 1980s [1]. Eigenface approach is one of the earliest appearance-based face recognition methods, which was developed by M. Turk and A. Pentland [1] in 1991. In this approach we have to perform a lots of computations, which are not feasible with respect to time in many real time system. The concept of principal component analysis (PCA) is used in this approach to reduce the dimension and hence reducing the computation time. Principal component analysis [4] decomposes face images into a small set of characteristic feature images called eigen faces.
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.
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.
Biometric Face Recognition using Principal Component Analysis
Biometric Face Recognition, a technology being used worldwide is of immense importance as it helps in ensuring public safety, preventing crimes and also improves customer experience in several fields. It is a software application which is capable of identification or distinct verification of an individual by analyzing and then comparing the pattern projected by the person's facial contours. This paper discusses the construction of a face recognition system using Principal component analysis (PCA) which is a statistical approach used to reduce the number of variables and complications which could occur during the process of face recognition while extracting the foremost relevant information from the image of the face being recognized. A training database of around 200-300 images belonging to different individuals is being used from an open source named face94 for carrying out the face recognition by comparing these images to the face to be recognized. This recognition is done by projecting a new test image onto the subspace which is then classified by measuring the minimum Manhattan distance.
Pca Based Efficient Face Recognition Technique
2018
Face recognition has many important applications e.g. recognition of faces at security checkpoints and airports. Human beings have capability of recognizing a person or a face but machine is not able to perform the same. The main aim is to engineer a system which is able to function like mankind. In this paper face recognition approach is proposed using Principal Component Analysis in the combination of Euclidian, city block distance and Mahalanobis distance.
Face Recognition using Neural Network & Principal Component Analysis
2014
The Human face image is contexture multidimensional point of perception version and by developing computational version for face recollection is rigid. The paper presents two methods for face identification, feature extraction is first method and classification is the second method. The classification is based on the Neural Network and feature is extraction is by Principal Component Analysis. The relevant information can be extracted by using the Eigenfaces, which are tenacious for face recognition. For face image identification the Eigenface image recognition the Eigen face perspective uses Principal Component Analysis (PCA) algorithm. The proposed system tested on 165 images from Yale face database. Test results gave a recognition rate above the 97%.
Security is one of the most important aspects in today’s computer environment. Especially, person authentication now a day is necessary to maintain security of computer based systems. Biometric authentication methods are becoming popular since last decade. Face recognition is one of the mature and popular biometric authentication methods. Today, with this paper, discussion on identifying best suitable samples for generating training database has been done. PCA based face recognition approach using Eigenface method has been discussed for the said purpose
Analyzing Face Recognition Using Pca and Comparison between Different Distance Classifier.
2013
Principal component analysis (PCA) is a widely used technique that is quite efficient and reliable when used for face recognition. Face is the most dominant feature that strongly communicates identity of the person. It proves to be very useful and secure if used for biometric identification. Principal component analysis uses Eigen Face approach for extracting effective features (Eigen vectors) from a given face database and these features corresponds to the dissimilarities among the faces. Every face in the database can be represented as a linear combination of these eigenvectors with appropriate weight assoc presents a methodology for face
Face Recognition System - PCA, Eigenface and Euclidean Distance Approach
Face recognition technology has been one of the most important fields that emerged during past two decades since the demand for identifying a person by analysing an image escalated exponentially. A face recognition system is a computer application which identifies and verify a person’s face automatically from a digital image. To successfully identify a face, a given face’s facial features would be compared to already existing face database’s facial features. The most similar image would be selected and presented as the identified face for the given face. Facial recognition could be used in many applications of security stream such as passport photo verification, access control, payment verifications, criminal identification and many more. In this project, a generic face recognition application is developed which could be adopted in many streams. There are there main phases in a face recognition system. First phase is acquiring images and pre-possessing them. Pre-processing images would help to reduce the drastic changes of images with the illumination of each input image. Furthermore it would help to process the images easily by reducing dimensions and would increase the accuracy of identifying a face and decrease the processing time. The second Phase is training the data set. It is important to have a database of faces of each individual which we can use to compare with the input face. The last phase is identification of a given face. Principal Component Analysis (PCA) is a commonly used feature extraction technique and in this project I have illustrated how it is implemented to reduce the dimensions and how it could work with Euclidian distance image classifier to identify a person’s image successfully.
DESIGN OF FACE RECOGNITION SYSTEM USING PRINCIPAL
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.