scholarly journals Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 517
Author(s):  
Xinting Li ◽  
Weijin Cheng ◽  
Chengsheng Yuan ◽  
Wei Gu ◽  
Baochen Yang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Feature Fusion ◽  
Texture Feature ◽  
Fusion Rule ◽  
Support Vector ◽  
Svm Classifier ◽  
Fine Grained ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Intelligent Devices

Currently, intelligent devices with fingerprint identification are widely deployed in our daily life. However, they are vulnerable to attack by fake fingerprints made of special materials. To elevate the security of these intelligent devices, many fingerprint liveness detection (FLD) algorithms have been explored. In this paper, we propose a novel detection structure to discriminate genuine or fake fingerprints. First, to describe the subtle differences between them and take advantage of texture descriptors, three types of different fine-grained texture feature extraction algorithms are used. Next, we develop a feature fusion rule, including five operations, to better integrate the above features. Finally, those fused features are fed into a support vector machine (SVM) classifier for subsequent classification. Data analysis on three standard fingerprint datasets indicates that the performance of our method outperforms other FLD methods proposed in recent literature. Moreover, data analysis results of blind materials are also reported.

An Enhanced Facial Expression Recognition Model Using Local Feature Fusion of Gabor Wavelets and Local Directionality Patterns

2020 ◽  
Vol 11 (1) ◽  
pp. 48-70 ◽  
Author(s):  
Sivaiah Bellamkonda ◽  
Gopalan N.P
Keyword(s):  
Facial Expression ◽  
Feature Fusion ◽  
Support Vector ◽  
Svm Classifier ◽  
Source Image ◽  
Expression Recognition ◽  
Fusion Model ◽  
Average Accuracy ◽  
Benchmark Datasets ◽  
Operator Fatigue

Facial expression analysis and recognition has gained popularity in the last few years for its challenging nature and broad area of applications like HCI, pain detection, operator fatigue detection, surveillance, etc. The key of real-time FER system is exploiting its variety of features extracted from the source image. In this article, three different features viz. local binary pattern, Gabor, and local directionality pattern were exploited to perform feature fusion and two classification algorithms viz. support vector machines and artificial neural networks were used to validate the proposed model on benchmark datasets. The classification accuracy has been improved in the proposed feature fusion of Gabor and LDP features with SVM classifier, recorded an average accuracy of 93.83% on JAFFE, 95.83% on CK and 96.50% on MMI. The recognition rates were compared with the existing studies in the literature and found that the proposed feature fusion model has improved the performance.

Face liveness detection scheme with static and dynamic features

2018 ◽  
Vol 16 (02) ◽  
pp. 1840001 ◽  
Author(s):  
Lifang Wu ◽  
Yaowen Xu ◽  
Meng Jian ◽  
Xiao Xu ◽  
Wei Qi
Keyword(s):  
Support Vector ◽  
Svm Classifier ◽  
Dynamic Features ◽  
Detection Scheme ◽  
Liveness Detection ◽  
Current State ◽  
Significant Research ◽  
The Face ◽  
Frame Motion ◽  
Fully Connected

Face liveness detection is a significant research topic in face-based online authentication. The current face liveness detection approaches utilize either static or dynamic features, but not both. In fact, the dynamic and static features have different advantages in face liveness detection. In this paper, we propose a scheme combining dynamic and static features to capture merits of them for face liveness detection. First, the dynamic maps are captured from the inter-frame motion in the video, which investigates motion information of the face in the video. Then, with a Convolutional Neural Network (CNN), the dynamic and static features are extracted from the dynamic maps and the frame images, respectively. Next, in CNN, the fully connected layers containing the dynamic and static features are concatenated to form a fused feature. Finally, the fused features are used to train a binary Support Vector Machine (SVM) classifier, which classifies the frames into two categories, i.e. frame with real or fake face. Experimental results and the corresponding analysis demonstrate that the proposed scheme is capable of discovering face liveness by fusing dynamic and static features and it outperforms the current state-of-the-art face liveness detection approaches.

A Novel Classification Method of Fish Based on Multi-Feature Fusion

2015 ◽  
Vol 713-715 ◽  
pp. 1513-1519 ◽  
Author(s):  
Wei Dong Du ◽  
Bao Wei Chen ◽  
Hai Sen Li ◽  
Chao Xu
Keyword(s):  
Discrete Cosine Transform ◽  
Feature Fusion ◽  
Acoustic Scattering ◽  
Scattering Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Correct Identification ◽  
Classification Problems ◽  
Cosine Transform ◽  
Identification Rate

In order to solve fish classification problems based on acoustic scattering data, temporal centroid (TC) features and discrete cosine transform (DCT) coefficients features used to analyze acoustic scattering characteristics of fish from different aspects are extracted. The extracted features of fish are reduced in dimension and fused, and support vector machine (SVM) classifier is used to classify and identify the fishes. Three kinds of different fishes are selected as research objects in this paper, the correct identification rates are given based on temporal centroid features and discrete cosine transform coefficients features and fused features. The processing results of actual experimental data show that multi-feature fusion method can improve the identification rate at about 5% effectively.

scholarly journals Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yujia Jiang ◽  
Xin Liu
Keyword(s):  
Local Binary Pattern ◽  
Fingerprint Recognition ◽  
Svm Classifier ◽  
Liveness Detection ◽  
Gaussian Pyramid ◽  
Detection Approach ◽  
Fingerprint Liveness Detection ◽  
Recognition Systems ◽  
Uniform Local Binary Pattern ◽  
High Recognition Accuracy

Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP) in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.

scholarly journals Local Non Zero Eigen Value Preservation Based Expression Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 3823-3832
Keyword(s):  
Discriminant Analysis ◽  
Feature Vector ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Fusion Rule ◽  
Classification Performance ◽  
Support Vector ◽  
Expression Recognition ◽  
Fisher Discriminant Analysis ◽  
Fisher Discriminant

This work proposes an finest mapping from features space to inherited space using kernel locality non zero eigen values protecting Fisher discriminant analysis subspace approach. This approach is designed by cascading analytical and non-inherited face texture features. Both Gabor magnitude feature vector (GMFV) and phase feature vector (GPFV) are independently accessed. Feature fusion is carried out by cascading geometrical distance feature vector (GDFV) with Gabor magnitude and phase vectors. Feature fusion dataset space is converted into short dimensional inherited space by kernel locality protecting Fisher discriminant analysis method and projected space is normalized by suitable normalization technique to prevent dissimilarity between scores. Final scores of projected domains are fused using greatest fusion rule. Expressions are classified using Euclidean distance matching and support vector machine radial basis function kernel classifier. An experimental outcome emphasizes that the proposed approach is efficient for dimension reduction, competent recognition and classification. Performance of proposed approach is deliberated in comparison with connected subspace approaches. The finest average recognition rate achieves 97.61% for JAFFE and 81.48% YALE database respectively.

A FRAMEWORK FOR FINGERPRINT LIVENESS DETECTION USING SUPPORT VECTOR MACHINE OPTIMIZED BY GENETIC ALGORITHM

2018 ◽  
Vol 5 (2) ◽  
pp. 1
Author(s):  
YUSUF IBRAHIM ◽  
MUHAMMED B. MU'AZU ◽  
EMMANUEL A. ADEDOKUN ◽  
YUSUF A. SHA'ABAN ◽  
◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Support Vector ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection

scholarly journals Recognition of Fetal Facial Ultrasound Standard Plane Based on Texture Feature Fusion

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiaoli Wang ◽  
Zhonghua Liu ◽  
Yongzhao Du ◽  
Yong Diao ◽  
Peizhong Liu ◽  
...  
Keyword(s):  
Cleft Lip ◽  
Cleft Lip And Palate ◽  
Feature Fusion ◽  
Texture Feature ◽  
Recall Rate ◽  
Support Vector ◽  
Ultrasound Images ◽  
Fusion Method ◽  
Accurate Identification ◽  
Data Set

In the process of prenatal ultrasound diagnosis, accurate identification of fetal facial ultrasound standard plane (FFUSP) is essential for accurate facial deformity detection and disease screening, such as cleft lip and palate detection and Down syndrome screening check. However, the traditional method of obtaining standard planes is manual screening by doctors. Due to different levels of doctors, this method often leads to large errors in the results. Therefore, in this study, we propose a texture feature fusion method (LH-SVM) for automatic recognition and classification of FFUSP. First, extract image’s texture features, including Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG), then perform feature fusion, and finally adopt Support Vector Machine (SVM) for predictive classification. In our study, we used fetal facial ultrasound images from 20 to 24 weeks of gestation as experimental data for a total of 943 standard plane images (221 ocular axial planes, 298 median sagittal planes, 424 nasolabial coronal planes, and 350 nonstandard planes, OAP, MSP, NCP, N-SP). Based on this data set, we performed five-fold cross-validation. The final test results show that the accuracy rate of the proposed method for FFUSP classification is 94.67%, the average precision rate is 94.27%, the average recall rate is 93.88%, and the average F 1 score is 94.08%. The experimental results indicate that the texture feature fusion method can effectively predict and classify FFUSP, which provides an essential basis for clinical research on the automatic detection method of FFUSP.

scholarly journals A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 795
Author(s):  
Dongbing Yu ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Green Tea ◽  
Electronic Nose ◽  
Classification Performance ◽  
Small Sample ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrimination Ability ◽  
Fine Grained

Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

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