Palmprint Authentication System Based on Local and Global Feature Fusion Using DOST

Palmprint is the region between wrist and fingers. In this paper, a palmprint personal identification system is proposed based on the local and global information fusion. The local and global information is critical for the image observation based on the results of the relationship between physical stimuli and perceptions. The local features of the enhanced palmprint are extracted using discrete orthonormal Stockwell transform. The global feature is obtained by reducing the scale of discrete orthonormal Stockwell transform to infinity. The local and global matching distances of the two palmprint images are fused to get the final matching distance of the proposed scheme. The results show that the fusion of local and global features outperforms the existing works on the available three datasets.

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Text Sentiment Classification Based on Feature Fusion

Revue d intelligence artificielle ◽

10.18280/ria.340418 ◽

2020 ◽

Vol 34 (4) ◽

pp. 515-520

Author(s):

Chen Zhang ◽

Qingxu Li ◽

Xue Cheng

Keyword(s):

Hybrid Model ◽

Short Term Memory ◽

Feature Fusion ◽

Contextual Information ◽

Sentiment Classification ◽

Global Features ◽

Softmax Classifier ◽

Parallel Hybrid ◽

Local And Global Features ◽

Lstm Network

The convolutional neural network (CNN) and long short-term memory (LSTM) network are adept at extracting local and global features, respectively. Both can achieve excellent classification effects. However, the CNN performs poorly in extracting the global contextual information of the text, while LSTM often overlooks the features hidden between words. For text sentiment classification, this paper combines the CNN with bidirectional LSTM (BiLSTM) into a parallel hybrid model called CNN_BiLSTM. Firstly, the CNN was adopted to extract the local features of the text quickly. Next, the BiLSTM was employed to obtain the global text features containing contextual semantics. After that, the features extracted by the two neural networks (NNs) were fused, and processed by Softmax classifier for text sentiment classification. To verify its performance, the CNN_BiLSTM was compared with single NNs like CNN and LSTM, as well as other deep learning (DL) NNs through experiments. The experimental results show that the proposed parallel hybrid model outperformed the contrastive methods in F1-score and accuracy. Therefore, our model can solve text sentiment classification tasks effectively, and boast better practical value than other NNs.

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Fine reconstruction of underwater images for environmental feature fusion

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211039687 ◽

2021 ◽

Vol 18 (5) ◽

pp. 172988142110396

Author(s):

Tao Xu ◽

Jiyong Zhou ◽

Wentao Guo ◽

Lei Cai ◽

Yukun Ma

Keyword(s):

Image Reconstruction ◽

Feature Fusion ◽

Image Data ◽

Target Feature ◽

Global Features ◽

Data Set ◽

Underwater Image ◽

Reconstruction Methods ◽

Local And Global Features ◽

Underwater Target

Complicated underwater environments, such as occlusion by foreign objects and dim light, causes serious loss of underwater targets feature. Furthermore, underwater ripples cause target deformation, which greatly increases the difficulty of feature extraction. Then, existing image reconstruction models cannot effectively achieve target reconstruction due to insufficient underwater target features, and there is a blurred texture in the reconstructed area. To solve the above problems, a fine reconstruction of underwater images with the target feature missing from the environment feature was proposed. Firstly, the salient features of underwater images are obtained in terms of positive and negative sample learning. Secondly, a layered environmental attention mechanism is proposed to retrieve the relevant local and global features in the context. Finally, a coarse-to-fine image reconstruction model, with gradient penalty constraints, is constructed to obtain the fine restoration results. Contrast experiment between the proposed algorithm and the existing image reconstruction methods has been done in stereo quantitative underwater image data set, real-world underwater image enhancement data set, and underwater image data set, clearly proving that the proposed one is more effective and superior.

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A NOVEL BIOMECHANICS-BASED APPROACH FOR PERSON RE-IDENTIFICATION BY GENERATING DENSE COLOR SIFT SALIENCE FEATURES

Journal of Mechanics in Medicine and Biology ◽

10.1142/s0219519417400115 ◽

2017 ◽

Vol 17 (07) ◽

pp. 1740011 ◽

Cited By ~ 14

Author(s):

JAMAL HUSSAIN SHAH ◽

ZONGHAI CHEN ◽

MUHAMMAD SHARIF ◽

MUSSARAT YASMIN ◽

STEVEN LAWRENCE FERNANDES

Keyword(s):

Convex Hull ◽

Nearest Neighbor ◽

Identification Accuracy ◽

Significant Feature ◽

Identification System ◽

Feature Descriptor ◽

Global Features ◽

Outlier Test ◽

Local And Global Features ◽

Unsolved Issue

Currently, identifying humans using biomechanics-based approaches has gained a lot of significance for person re-identification. Biomechanics-based approaches use knee-hip angle–angle relationships and body movements for person re-identification. Generally, biomechanics of human walking and running is used for person re-identification. In fact, person re-identification is a complex and important task in academia as well as industry and remains an unsolved issue in the computer vision field. The subjects most commonly addressed regarding person re-identification include significant feature extraction that can function accurately with invariant appearance and robust classification. In this study, a significant color feature descriptor is proposed by combining dense color-SIFT and global convex hull salience region features. First convex hull boundary points are detected using the SIFT technique. Furthermore, it is extended with Grubb’s outlier test to eliminate the outlier points detected by SIFT and mark the saliency region via convex hull. Then dense-SIFT and dense-CHF methods are used to extract local and global features within the convex hull region, respectively. Finally, the pre-ranked common nearest neighbor selection technique is applied to minimize overhead of dataset and generate more robust rank classification. The proposed technique is tested using three-camera database video sequences and three publicly available datasets, namely i-LIDS, VIPeR and GRID. Performance of re-identification system is evaluated using a statistical method with CMC curves. The results show better re-identification accuracy in solving the aforementioned problems.

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A Multichannel Model for Microbial Key Event Extraction Based on Feature Fusion and Attention Mechanism

Security and Communication Networks ◽

10.1155/2021/7800144 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Peng Li ◽

Qian Wang

Keyword(s):

Sentiment Analysis ◽

Feature Fusion ◽

Attention Mechanism ◽

Event Extraction ◽

Semantic Features ◽

Analysis Model ◽

Global Features ◽

Public Health Emergencies ◽

Multimodal Information ◽

Local And Global Features

In order to further mine the deep semantic information of the microbial text of public health emergencies, this paper proposes a multichannel microbial sentiment analysis model MCMF-A. Firstly, we use word2vec and fastText to generate word vectors in the feature vector embedding layer and fuse them with lexical and location feature vectors; secondly, we build a multichannel layer based on CNN and BiLSTM to extract local and global features of the microbial text; then we build an attention mechanism layer to extract the important semantic features of the microbial text; thirdly, we merge the multichannel output in the fusion layer and use soft; finally, the results are merged in the fusion layer, and a surtax function is used in the output layer for sentiment classification. The results show that the F1 value of the MCMF-A sentiment analysis model reaches 90.21%, which is 9.71% and 9.14% higher than the benchmark CNN and BiLSTM models, respectively. The constructed dataset is small in size, and the multimodal information such as images and speech has not been considered.

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Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/305 ◽

2017 ◽

Cited By ~ 144

Author(s):

Wei Li ◽

Xiatian Zhu ◽

Shaogang Gong

Keyword(s):

Neural Network ◽

Feature Selection ◽

State Of The Art ◽

Feature Representation ◽

Global Feature ◽

Global Features ◽

Joint Learning ◽

Discriminative Feature ◽

Wide Range ◽

Local And Global Features

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).

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Weakly Supervised Local-Global Relation Network for Facial Expression Recognition

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/145 ◽

2020 ◽

Author(s):

Haifeng Zhang ◽

Wen Su ◽

Jun Yu ◽

Zengfu Wang

Keyword(s):

Facial Expression ◽

Feature Fusion ◽

Local Features ◽

Relational Reasoning ◽

Expression Recognition ◽

Global Features ◽

Global Relation ◽

Weighted Fusion ◽

Local And Global Features ◽

Weakly Supervised

To extract crucial local features and enhance the complementary relation between local and global features, this paper proposes a Weakly Supervised Local-Global Relation Network (WS-LGRN), which uses the attention mechanism to deal with part location and feature fusion problems. Firstly, the Attention Map Generator quickly finds the local regions-of-interest under the supervision of image-level labels. Secondly, bilinear attention pooling is employed to generate and refine local features. Thirdly, Relational Reasoning Unit is designed to model the relation among all features before making classification. The weighted fusion mechanism in the Relational Reasoning Unit makes the model benefit from the complementary advantages between different features. In addition, contrastive losses are introduced for local and global features to increase the inter-class dispersion and intra-class compactness at different granularities. Experiments on lab-controlled and real-world facial expression dataset show that WS-LGRN achieves state-of-the-art performance, which demonstrates its superiority in FER.

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