2DPCA Features Research Articles

Microwave Heartprint: A novel non-contact human identification technology based on cardiac micro-motion detection using ultra wideband bio-radar

The existing one-time identity authentication technology cannot continuously guarantee the legitimacy of user identity during the whole human-computer interaction session, and often requires active cooperation of users, which seriously limits the availability. This study proposes a new non-contact identity recognition technology based on cardiac micro-motion detection using ultra wideband (UWB) bio-radar. After the multi-point micro-motion echoes in the range dimension of the human heart surface area were continuously detected by ultra wideband bio-radar, the two-dimensional principal component analysis (2D-PCA) was exploited to extract the compressed features of the two-dimensional image matrix, namely the distance channel-heart beat sampling point (DC-HBP) matrix, in each accurate segmented heart beat cycle for identity recognition. In the practical measurement experiment, based on the proposed multi-range-bin & 2D-PCA feature scheme along with two conventional reference feature schemes, three typical classifiers were selected as representatives to conduct the heart beat identification under two states of normal breathing and breath holding. The results showed that the multi-range-bin & 2D-PCA feature scheme proposed in this paper showed the best recognition effect. Compared with the optimal range-bin & overall heart beat feature scheme, our proposed scheme held an overall average recognition accuracy of 6.16% higher (normal respiration: 6.84%; breath holding: 5.48%). Compared with the multi-distance unit & whole heart beat feature scheme, the overall average accuracy increase was 27.42% (normal respiration: 28.63%; breath holding: 26.21%) for our proposed scheme. This study is expected to provide a new method of undisturbed, all-weather, non-contact and continuous identification for authentication.

PEMBUATAN PERANGKAT LUNAK UNTUK KLASIFIKASI JENIS KELAMIN BERDASARKAN CITRA WAJAH

In this study, the 2DPCA feature extraction method and SOM classification were used to classify human sex based on facial images. The trial was conducted on 100 facial images consisting of 50 male facial images and 50 female facial images. The 100 facial image data used were divided into 70 training data and 30 test data. Based on the test results, the best accuracy obtained is 86.67% using a start learning rate of 0.6 and a maximum iteration of 10000. level of accuracy, where a larger maximum iteration value results in a higher level of accuracy.

Design and analysis of various bidirectional 2DPCAs in feature partitioning framework

In this paper, we present various of bidirectional 2DPCAs in feature partitioning framework. To capture hybrid variations of images with optimal feature dimensions, we propose bidirectional sub-image PCA (Bi-SIMPCA), bidirectional flexible PCA (Bi-FLPCA), bidirectional extended SIMPCA (Bi-ESIMPCA) and bidirectional extended FLPCA (Bi-EFLPCA). The Bi-SIMPCA operates on a set of partitioned sub-images of original image and extracts the 2DPCA feature by simultaneously considering the row and column directions of sub-images. Bi-SIMPCA neglects the cross-correlation across sub-images. The Bi-FLPCA further improves the Bi-SIMPCA by removing the redundant features using cross-correlation across sub-images. The Bi-ESIMPCA instead operates on the sub-images of an image and the same whole image simultaneously and extracts the 2DPCA features along row and column directions. Bi-ESIMPCA lacks the global cross-correlation across sub-images and whole images. The Bi-EFLPCA further improves the Bi-ESIMPCA by reducing the redundant features using global correlation across sub-images and whole images. The issues, such as summarization of variance, space and time complexities of the proposed methods are investigated as well. The simulation results using YALE and ORL facial datasets with variable image resolutions show the superior performance of Bi-EFLPCA method with respect to feature dimensionality, memory efficiency, recognition accuracy, and speed over the existing variation of bidirectional 2DPCAs.

Optimization of face recognition algorithm based on deep learning multi feature fusion driven by big data

Today, with the rapid development of science and technology, the era of big data has been proposed and triggered reforms in all walks of life. Face recognition is a biometric recognition method with the characteristics of non-contact, non mandatory, friendly and harmonious, which has a good application prospect in the fields of national security and social security. With the deepening of the research on face recognition, small-scale face recognition has achieved good recognition results, but in the era of big data, the existing small-scale face recognition methods have gradually failed to meet the social needs, and how to get a good face recognition effect in the era of big data has become a new research hotspot. Based on this, this paper aims to optimize the existing face recognition algorithm, study the face recognition method driven by big data, and propose a deep learning multi feature fusion face recognition algorithm driven by big data. First, for the problem that 2DPCA (Two-dimensional Principle Component Analysis) can well extract the global features of the face under large samples, but the local features of the face are difficult to process, this paper uses the LBP (Local Binary Pattern, LBP) algorithm to extract the texture features of the face, and the extracted texture features are integrated with the global features extracted by 2DPCA to multi-feature fusion, so that the fused features can take into account both global and local features, and have better recognition results. Then using the obtained fusion features as input, training in a convolutional neural network, and measuring the similarity based on the feature vectors of the sample set and the training set after the training, can realize multi-feature fusion face recognition. Through the analysis of simulation experiments, it is found that, compared with the use of global features or local features alone, the fusion features obtained by multi-feature fusion of global features extracted by 2DPCA and local features extracted by LBP algorithm have better recognition effect in the big data environment. After convolutional neural network trains and recognizes this feature, a high recognition accuracy rate is obtained, which can show that the face recognition method designed in this paper has good application potential in the era of big data. In the background of big data, the accuracy of face recognition can reach more than 90%, which can meet the needs of society well.

Improving recognition accuracy of partial discharge patterns by image-oriented feature extraction and selection technique

This paper aims to improve recognition accuracies of single sources of partial discharge (PD) by employing image-oriented feature extraction and selection algorithms. 419 diversified samples of PD data acquired from typically artificial defect models, where the defect size, applied voltage and insulation aging are taken into account, are employed for algorithms validation. In the proposed method, the PD gray images are decomposed into various vectors by two-dimensional principal component analysis (2DPCA) on horizontal and vertical directions, respectively, in which 9 representative parameters are extracted from each image vector. Based on the 419 PD samples, the proposed image features are confirmed to achieve better PD recognition performances than typical image compression methods relevant to 2DPCA. To further improve the recognition performance and reduce the feature dimension, the feature selection technique based on non-dominated sorting genetic algorithm II (NSGA-II) is adopted to search the optimal 2DPCA features. By using the proposed image-oriented feature extraction and selection algorithm, fuzzy k-nearest neighbor classifier (FkNNC), back-propagation neural network (BPNN) and support vector machine (SVM) with 16-dimensional 2DPCA features can now achieve 96.4%, 94.4% and 97.5% accuracies, respectively. The increment of average recognition accuracies of 5%–7% are obtained by different classifiers compared with phase-resolved partial discharge (PRPD) statistical features in previous studies. The investigations on the recognition results relevant to image distortion, cavity discharge change with time and PD contaminated by random noises with different level confirm the robustness of the 16-dimensional 2DPCA features, which signify a great potential in applying the proposed method to field PD pattern recognition in the future.

Nonparametric subspace analysis fused to 2DPCA for face recognition

Two-dimensional principal component analysis (2DPCA) is one of the representative techniques for image representation and recognition. However, keen storage requirements and computational complexity consist in 2DPCA. Meanwhile, the performance of 2DPCA is delicate in illumination variations. Nonparametric subspace analysis (NSA) is a subspace learning method that can reduce dimensionality and identify local information for discrimination, so that it can make 2DPCA perform well in illumination. Motivated by above facts, 2DPCA fused with NSA is implemented for face recognition, which can reduce dimensions of the 2DPCA feature vectors and enhance the contribution of principal components to face recognition. Experiments carried out on ORL, Yale B, and FERET facial databases show that valid recognition rates can be achieved by the proposed method compared to 2DPCA, 2DPCA plus PCA, LDA methods and demonstrate promising abilities against illumination variations.

Color face recognition based on quaternion matrix representation

There are several methods to recognize and reconstruct a human face image. The principal component analysis (PCA) is a successful approach because of its effective extraction of the global feature and excellent reconstruction of face image. However, the crucial shortcomings of PCA are its low recognition rate and overfitting of feature extraction which leads to the dependence of training data on training samples. In this paper, a modified two-dimension principal component analysis (2DPCA) and bidirectional principal component analysis (BDPCA) methods based on quaternion matrix are proposed to recognize and reconstruct a color face image. In these methods, the spatial distribution information of color images is used to represent a color face, and the 2DPCA or BDPCA feature of color face image is extracted by reducing the dimensionality in both column and row directions. A method obtaining orthogonal eigenvector set of quaternion matrix is proposed. Numerous experiments show that the present approach based on quaternion matrix can effectively smooth the overfitting issue and substantially enhance the recognition rate.

Features Fusion Based on FLD for Face Recognition

In this paper, we introduced a features fusion method for face recognition based on Fisher’s Linear Discriminant (FLD). The method extract features by employed Two-Dimensional principal component analysis (2DPCA) and Gabor wavelets, and then fuse their features which are extracted with FLD respectively. As a holistic feature extraction method, 2DPCA performs dimensional reduction to the input dataset while retaining characteristics of the dataset that contribute most to its variance by eliminating the later principal components. On the contrary, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale and orientation selectivity, which produce salient local features which are most suitable for face recognition. So, we use Gabor wavelets for the local features and then integrate Gabor features with 2DPCA features. In addition to, because FLD could make not only the scatter between classes as large as possible, but the scatter within class as small as possible, the features which are extracted by FLD are reliable for classification. And then, the FLD features of the Gabor and 2DPCA features leads to the application of the support vector machine (SVM) for classification. Finally, the computer simulation illustrates the effectivity of this method on three independent face databases.