scholarly journals Human Face Recognition using Gabor Based Kernel Entropy Component Analysis

2012 ◽  
Vol 2 (3) ◽  
pp. 1-20 ◽  
Author(s):  
Arindam Kar ◽  
Debotosh Bhattacharjee ◽  
Dipak Kumar Basu ◽  
Mita Nasipuri ◽  
Mahantapas Kundu
Keyword(s):  
Face Recognition ◽  
Distance Measure ◽  
Recognition Performance ◽  
Component Analysis ◽  
Gabor Wavelet ◽  
Distance Measures ◽  
Spatial Locality ◽  
Gabor Wavelet Transformation ◽  
Facial Images ◽  
Entropy Component

In this paper, the authors present a novel Gabor wavelet based Kernel Entropy Component Analysis (KECA) method by integrating the Gabor wavelet transformation (GWT) of facial images with the KECA method for enhanced face recognition performance. Firstly, from the Gabor wavelet transformed images the most important discriminative desirable facial features characterized by spatial frequency, spatial locality and orientation selectivity to cope with the variations due to illumination and facial expression changes were derived. After that KECA, relating to the Renyi entropy is extended to include cosine kernel function. The KECA with the cosine kernels is then applied on the extracted most important discriminating feature vectors of facial images to obtain only those real kernel ECA eigenvectors that are associated with eigenvalues having positive entropy contribution. Finally, these real KECA features are used for image classification using the L1, L2 distance measures; the Mahalanobis distance measure and the cosine similarity measure. The feasibility of the Gabor based KECA method with the cosine kernel has been successfully tested on both frontal and pose-angled face recognition, using datasets from the ORL, FRAV2D, and the FERET database.

scholarly journals High Performance Human Face Recognition using Gabor Based Pseudo Hidden Markov Model

2013 ◽  
Vol 4 (1) ◽  
pp. 81-102 ◽  
Author(s):  
Arindam Kar ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu ◽  
Mahantapas Kundu
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
High Performance ◽  
Hidden Markov ◽  
Gabor Wavelet ◽  
Data Set ◽  
Scanning Method ◽  
Face Images ◽  
Gabor Wavelet Transformation

This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov Model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e., a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D, and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.

scholarly journals Algorithm of Face Recognition by Principal Component Analysis

1970 ◽  
Vol 3 (2) ◽  
Author(s):  
Khalid A. S. Al-Khateeb and Jaiz A. Y. Johari
Keyword(s):  
Computer Vision ◽  
Principal Component Analysis ◽  
Face Recognition ◽  
Data Base ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
Computer Vision Applications ◽  
Simplified Algorithm ◽  
Facial Images

A face recognition algorithm based on Principal Component Analysis (PCA) has been developed and tested for computer vision applications. A database of about 400 facial images was used to test the algorithm. Each image is represented by a matrix (112 x 92), The data base is divided into subsets, where each subset represents one of 10 different individuals. A 96% rate of successful detection and a 90% rate of successful recognition were obtained. Several factors had to be standardized to provide a constrained environment in order to reduce error. The analysis is based on a set of eigenvectors that defines an Eigen Face (EF). The method proved to be simple and effective. The simplified algorithm and techniques expedited the process without seriously compromising the accuracy.

scholarly journals Pengenalan Wajah Pada Sistem Presensi Menggunakan Metode Dynamic Times Wrapping, Principal Component Analysis dan Gabor Wavelet

2016 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Romi Wiryadinata ◽  
Raya Sagita ◽  
Siswo Wardoyo ◽  
Priswanto Priswanto
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Face Image ◽  
Gabor Wavelet ◽  
Computer Application ◽  
Facial Image ◽  
Presence Data ◽  
Normal Expression

<p align="justify">Presensi is a logging attendance, part of activity reporting an institution, or a component institution itself which contains the presence data compiled and arranged so that it is easy to search for and used when required at any time by the parties concerned. Computer application developed in the presensi system is a computer application that can recognize a person's face using only a webcam. Face recognition in this study using a webcam to capture an image of the room at any given time who later identified the existing faces. Some of the methods used in the research here is a method of the Dynamic Times Wrapping (DTW), Principal Component Analysis (PCA) and Gabor Wavelet. This system, used in testing with normal facial image expression. The success rate of the introduction with the normal expression of face image using DTW amounting to 80%, 100% and PCA Gabor wavelet 97%</p>

Face recognition using kernel entropy component analysis

2011 ◽  
Vol 74 (6) ◽  
pp. 1053-1057 ◽  
Author(s):  
B.H. Shekar ◽  
M. Sharmila Kumari ◽  
Leonid M. Mestetskiy ◽  
Natalia F. Dyshkant
Keyword(s):  
Face Recognition ◽  
Component Analysis ◽  
Entropy Component

Two-Dimensional Parameter Principal Component Analysis for Face Recognition

2014 ◽  
Vol 971-973 ◽  
pp. 1838-1842 ◽  
Author(s):  
Qi Rong Zhang
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Recognition Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Image Feature ◽  
Two Dimensional ◽  
Face Database ◽  
Dimensional Parameter ◽  
Image Feature Extraction

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional parameter principal component analysis (2DPPCA). Two-dimensional principal component analysis (2DPCA) is widely used in face recognition. We further study on the basis of 2DPCA. This proposed method is to add a parameter to images samples matrix in the image covariance matrix. Extensive experiments are performed on FERET face database and CMU PIE face database. The 2DPPCA method achieves better face recognition performance than PCA, 2DPCA, especially on the CMU PIE face database.

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