Face Recognition Using Pca, Lda and Various Distance Classifiers (original) (raw)

Face Recognition using PCA and LDA Comparative Study

Biometrics is a system in which we used to recognise human on the basis of its physical or behavioural characteristics. Today all over the world every country wants security of data, physical access, etc. Face recognition is widely accepted technique in human being same things are used in computer vision by using the image processing. In this paper we have used Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for extraction of features. Techniques has been applied for identification of a person on various databases such as ORL, Indian & KVKR and distance is calculated by using Euclidian Distance between training images and testing images. In this experiment total 1266 images used apart from that 25 subject from KVKR Face database this database is developed under UGC-SAP Phase I (our own major contribution) having 10 pose of each subject, 40 subjects from ORL having 10 images each, and from IIT Indian database 56 subjects, 11 images per subjects. After apply PCA on ORL Database the highest RR 97.50% for experiment 1:8 images, on KVKR-Face Database we got 92.00% RR for 1:9 and on IIT-Indian database 62.50%RR for 1:9. After applied LDA on ORL database we got the result 80.00% is highest RR, also applied on KVKR-Face database we got excellent result 100% RR and on IIT-Indian database 64.29% RR.

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 using principle components and linear discriminant analysis

2009

Face recognition has recently received significant attention as one of the challenging and promising fields of computer vision and pattern recognition. It plays a significant role in many security and forensic applications such as person authentication in access control systems and person identification in real time video surveillance systems. This paper studies two appearance-based approaches for feature extraction and dimension reduction, namely, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Numerical experiments were carried out on the ORL face database and many parameters were investigated, this included the effect of changing the number of training images, scaling factor, and the effect of feature vector length on the recognition rate. Classification is performed using the minimum Euclidean distance. The results suggest that the effect of increasing the number of training images has more significance on the recognition rate than changing the image ...

Face recognition system based on principal components analysis and distance measures

International Journal of Engineering & Technology, 2018

Face recognition plays a vital role and has a huge scope in the field of biometrics, image processing, artificial intelligence, pattern recognition and computer vision. This paper presents an approach to perform face recognition using Principal Components Analysis (PCA) as feature extraction technique and different distance measures as matching techniques. The proposed method is developed after the deep study of a number of face recognition methods and their outcomes. In the proposed method, Principal Components Analysis is used for facial features extraction and data representation. It generates eigenvalues of the facial images, hence, reduces the dimensionality. The recognition is produced using three different matching techniques (Euclidean, Manhattan and Mahalanobis) and the results are` presented. Yale and Aberdeen Face Databases are used to test and analyze the results of the proposed method.

Face recognition using PCA and LDA: analysis and comparison

Fifth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom 2013), 2013

Computer recognition of the human faces has evolved as the most successful and demanding field in the computer science world and in particular Computer Vision. A lot of research work has been done in this field in the last two decade. Numerous algorithms have been projected and they have been experimented with different face image database available. In this paper, a comparative study has been carried out using the two basic and the most important appearance based face recognition methods viz, PCA and LDA. These two techniques for face recognition has been implemented and evaluated with different databases like UMIST, Yale etc. The outputs are compared by using accuracy rate.

Accurate Face Recognition Using PCA and LDA

res publication, 2011

Face recognition from images is a sub-area of the general object recognition problem. It is of particular interest in a wide variety of applications. Here, the face recognition is based on the new proposed modified PCA algorithm by using some components of the LDA algorithm of the face recognition. The proposed algorithm is based on the measure of the principal components of the faces and also to find the shortest distance between them. The experimental results demonstrate that this arithmetic can improve the face recognition rate.. Experimental results on ORL face database show that the method has higher correct recognition rate and higher recognition speeds than traditional PCA algorithm.

Comparison Of PCA And LDA For Face Recognition

International journal of engineering research and technology, 2013

Face recognition from images is a sub-area of the general object recognition problem. It is of particular interest in a wide variety of applications. Here, the face recognition is based on the new proposed modified PCA algorithm by using some components of the LDA algorithm of the face recognition. The experimental results demonstrate that this arithmetic can improve the face recognition rate. The aim is to show that LDA is better than PCA in face recognition. Face and facial feature detection plays an important role in various applications such as human computer interaction, video surveillance, face tracking, and face recognition. Face recognition not only makes hackers virtually impossible to steal one's " password" but also increases the user-friendliness in human-computer interaction. Apparently the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. KeywordsPCA, LDA, face recognition

A comparative study of feature extraction using PCA and LDA for face recognition

2011 7th International Conference on Information Assurance and Security (IAS), 2011

Feature extraction is important in face recognition. This paper presents a comparative study of reature extraction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for face recognition. The evaluation parameters for the study are time and accuracy of each method. The experiments were conducted using six datasets of face images with different disturbance. The results showed that LDA is much better than PCA in overall image with various disturbances. While in time taken evaluation, PCA is faster than LDA.