Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

A Gray-Scale Face Recognition Approach

Abstract: Face recognition has been a focus in research for the last couple of decades because of its wide potential applications and its importance to meet the security needs of today's world. However, the network is very complex and therefore it is difficult to train. To reduce complexity, neural network is often applied to the pattern recognition phase rather than to the feature extraction phase. In order to cope with such complication and find out the true invariant for recognition, researchers have developed various recognition algorithms. In this paper, we give a new face recognition algorithm based on gray-scale. The paper shows the readers the basic principle of this face recognition algorithm, the implement of the new face recognition approach, the advantage of the new algorithm, and shows higher recognition rate than eigenface algorithm in the same experiment conditions according to our practices.

Offline handwritten numeral recognition using orthogonal Gaussian mixture model

Abstract: In the statistical approach to offline handwritten numeral recognition, we use the Gaussian mixture model (GMM) to approximate arbitrary class conditional probability density. For simplification, the GMM is assumed to be diagonal covariance matrices. In the case of the features of handwritten numerals being correlated statistically, a large number of mixture components are usually needed to obtain a good approximation. To solve this problem, the feature vectors are first transformed to the space spanned by the eigenvectors of the covariance matrix so that the correlation among the elements is reduced, namely orthogonal transformation. This GMM is defined as orthogonal Gaussian mixture model (OGMM). Finally, the effectiveness of this algorithm is demonstrated by applying it to the NIST database.

Face Recognition in an Unconstrained and Real-Time Environment Using Novel BMC-LBPH Methods Incorporates with DJI Vision Sensor

Abstract: Face recognition (FR) in an unconstrained environment, such as low light, illumination variations, and bad weather is very challenging and still needs intensive further study. Previously, numerous experiments on FR in an unconstrained environment have been assessed using Eigenface, Fisherface, and Local binary pattern histogram (LBPH) algorithms. The result indicates that LBPH FR is the optimal one compared to others due to its robustness in various lighting conditions. However, no specific experiment has been conducted to identify the best setting of four parameters of LBPH, radius, neighbors, grid, and the threshold value, for FR techniques in terms of accuracy and computation time. Additionally, the overall performance of LBPH in the unconstrained environments are usually underestimated. Therefore, in this work, an in-depth experiment is carried out to evaluate the four LBPH parameters using two face datasets: Lamar University data base (LUDB) and 5_celebrity dataset, and a novel Bilateral Median Convolution-Local binary pattern histogram (BMC-LBPH) method was proposed and examined in real-time in rainy weather using an unmanned aerial vehicle (UAV) incorporates with 4 vision sensors. The experimental results showed that the proposed BMC-LBPH FR techniques outperformed the traditional LBPH methods by achieving the accuracy of 65%, 98%, and 78% in 5_celebrity dataset, LU dataset, and rainy weather, respectively. Ultimately, the proposed method provides a promising solution for facial recognition using UAV.

An adaptive neural networks formulation for the two-dimensional principal component analysis

Abstract: This study, for the first time, developed an adaptive neural networks (NNs) formulation for the two-dimensional principal component analysis (2DPCA), whose space complexity is far lower than that of its statistical version. Unlike the NNs formulation of principal component analysis (PCA, i.e., 1DPCA), the solution with lower iteration in nature aims to directly deal with original image matrices. We also put forward the consistence in the conceptions of `eigenfaces' or `eigengaits' in both 1DPCA and 2DPCA neural networks. To evaluate the performance of the proposed NN, the experiments were carried out on AR face database and on 64 × 64 pixels gait energy images on CASIA(B) gait database. The less reconstruction error was exploited using the proposed NN in the condition of a large sample set compared to adaptive estimation of learning algorithms for NNs of PCA. On the contrary, if the sample set was small, the proposed NN could achieve a higher residue error than PCA NNs. The amount of calculation for the proposed NN here could be smaller than that for the PCA NNs on the feature extraction of the same image matrix, which represented an efficient solution to the problem of training images directly. On face and gait recognition tasks, a simple nearest neighbor classifier test indicated a particular benefit of the neural network developed here which serves as an efficient alternative to conventional PCA NNs.

Deterioration of visual information in face classification using Eigenfaces and Fisherfaces

Abstract: In the area of biometrics, face classification becomes one of the most appealing and commonly used approaches for personal identification. There has been an ongoing quest for designing systems that exhibit high classification rates and portray significant robustness. This feature becomes of paramount relevance when dealing with noisy and uncertain images. The design of face recognition classifiers capable of operating in presence of deteriorated (noise affected) face images requires a careful quantification of deterioration of the existing approaches vis-a-vis anticipated form and levels of image distortion. The objective of this experimental study is to reveal some general relationships characterizing the performance of two commonly used face classifiers (that is Eigenfaces and Fisherfaces) in presence of deteriorated visual information. The findings obtained in our study are crucial to identify at which levels of noise the face classifiers can still be considered valid. Prior knowledge helps us develop adequate face recognition systems. We investigate several typical models of image distortion such as Gaussian noise, salt and pepper, and blurring effect and demonstrate their impact on the performance of the two main types of the classifiers. Several distance models derived from the Minkowski family of distances are investigated with respect to the produced classification rates. The experimental environment concerns a well-known standard in this area of face biometrics such as the FERET database. The study reports on the performance of the classifiers, which is based on a comprehensive suite of experiments and delivers several design hints supporting further developments of face classifiers.