Finger-vein and fingerprint recognition based on a feature-level fusion method

2013 ◽  
Author(s):  
Jinfeng Yang ◽  
Bofeng Hong
Keyword(s):  
Fingerprint Recognition ◽  
Fusion Method ◽  
Finger Vein ◽  
Feature Level Fusion ◽  
Level Fusion

scholarly journals PET/MR fusion texture analysis for the clinical outcome prediction in soft-tissue sarcoma

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Wenzhe Zhao ◽  
Xin Huang ◽  
Geliang Wang ◽  
Jianxin Guo
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Univariate Analysis ◽  
Multivariable Analysis ◽  
Classification Performance ◽  
Fusion Method ◽  
Mr Images ◽  
Feature Level Fusion ◽  
Optimal Classification ◽  
Level Fusion

Abstract Background Various fusion strategies (feature-level fusion, matrix-level fusion, and image-level fusion) were used to fuse PET and MR images, which might lead to different feature values and classification performance. The purpose of this study was to measure the classification capability of features extracted using various PET/MR fusion methods in a dataset of soft-tissue sarcoma (STS). Methods The retrospective dataset included 51 patients with histologically proven STS. All patients had pre-treatment PET and MR images. The image-level fusion was conducted using discrete wavelet transformation (DWT). During the DWT process, the MR weight was set as 0.1, 0.2, 0.3, 0.4, …, 0.9. And the corresponding PET weight was set as 1- (MR weight). The fused PET/MR images was generated using the inverse DWT. The matrix-level fusion was conducted by fusing the feature calculation matrix during the feature extracting process. The feature-level fusion was conducted by concatenating and averaging the features. We measured the predictive performance of features using univariate analysis and multivariable analysis. The univariate analysis included the Mann-Whitney U test and receiver operating characteristic (ROC) analysis. The multivariable analysis was used to develop the signatures by jointing the maximum relevance minimum redundancy method and multivariable logistic regression. The area under the ROC curve (AUC) value was calculated to evaluate the classification performance. Results By using the univariate analysis, the features extracted using image-level fusion method showed the optimal classification performance. For the multivariable analysis, the signatures developed using the image-level fusion-based features showed the best performance. For the T1/PET image-level fusion, the signature developed using the MR weight of 0.1 showed the optimal performance (0.9524(95% confidence interval (CI), 0.8413–0.9999)). For the T2/PET image-level fusion, the signature developed using the MR weight of 0.3 showed the optimal performance (0.9048(95%CI, 0.7356–0.9999)). Conclusions For the fusion of PET/MR images in patients with STS, the signatures developed using the image-level fusion-based features showed the optimal classification performance than the signatures developed using the feature-level fusion and matrix-level fusion-based features, as well as the single modality features. The image-level fusion method was more recommended to fuse PET/MR images in future radiomics studies.

Fault Diagnosis of Rolling Bearing Based on Feature-Level Fusion Method

2013 ◽  
Vol 273 ◽  
pp. 260-263
Author(s):  
Ling Li Jiang ◽  
Hua Kui Yin ◽  
Si Wen Tang
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Normal Operation ◽  
Fusion Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Study Results ◽  
Feature Level Fusion ◽  
Level Fusion

Bearing failure is one of the foremost causes of breakdowns in rotating machinery and such failure can be catastrophic. Fault diagnosis is critical to maintaining the normal operation of the bearings. This paper proposes feature-level fusion method for rolling bearing fault diagnosis. Features are extracted from eight vibration signals to constitute a fusion vector. SVM is used for pattern recognition. The case study results show that the proposed method is useful for rolling bearing fault diagnosis.

scholarly journals A Score Level Fusion Method on Fingerprint and Finger Vein

2020 ◽  
Vol 185 ◽  
pp. 03035
Author(s):  
Jian Peng ◽  
Jingyi Wu ◽  
Yun Chen
Keyword(s):  
Clustering Analysis ◽  
Feature Matching ◽  
Recognition Rate ◽  
Experimental Results ◽  
Identification System ◽  
Fusion Method ◽  
Score Level Fusion ◽  
Finger Vein ◽  
Score Range ◽  
Level Fusion

In this paper, we represent a score level fusion method on fingerprint and finger vein. Each unimodal identification system carries out processes of image preprocessing, feature extraction and feature matching to generate a vector of score. And we apply clustering analysis to split the score range into zones of interest. Then a decision tree and weighted-sum approach are used to make the decision. We test the proposed method on standard biometric database. Three metrics, namely, False Accept Rate, False Reject Rate, Recognition Rate, are used to evaluate experimental results. And experimental results show that the fusion system has a better performance than unimodal identification system.

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