Texture Driven Hierarchical Fusion for Multi-Biometric Sys-tem

This article presents hierarchical fusion models for multi-biometric systems with improved recognition rate. Isolated texture regions are used to encode spatial variations from the composite biometric image which is generated by signal level fusion scheme. In this paper, the prominent issues of the existing multi-biometric system, namely, fusion methodology, storage complexity, reliability and template security are discussed. Here wavelet decomposition driven multi-resolution approach is used to generate the composite images. Texture feature metrics are extracted from multi-level texture regions and principal component analyzes are evaluated to select potentially useful texture values during template creation. Here through consistency and correlation driven hierarchical feature selection both inter-class similarity and intra-class variance problems can be solved. Finally, t-normalized feature level fusion is incorporated as a last stage to create the most reliable template for the identification process. To ensure the security and add robustness to spoof attacks random key driven permutations are used to encrypt the generated multi-biometric templates before storing it in a database. Our experimental results proved that the proposed hierarchical fusion and feature selection approach can embed fine detailed information about the input multi modal biometric images with the least complex identification process.

Download Full-text

Face Recognition by PCA and Improved LBP Fusion Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.734.562 ◽

2015 ◽

Vol 734 ◽

pp. 562-567 ◽

Cited By ~ 3

Author(s):

En Zeng Dong ◽

Yan Hong Fu ◽

Ji Gang Tong

Keyword(s):

Face Recognition ◽

Recognition Rate ◽

Texture Feature ◽

Principal Component ◽

Texture Features ◽

Rotation Invariant ◽

Fusion Algorithm ◽

Face Images ◽

The Face ◽

Pca Method

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

Download Full-text

EMOTION ANALYSIS USING SIGNAL AND IMAGE PROCESSING APPROACH BY IMPLEMENTING DEEP NEURAL NETWORK

Biomedical Sciences Instrumentation ◽

10.34107/yhpn9422.04313 ◽

2021 ◽

Vol 57 (2) ◽

pp. 313-321

Author(s):

S Shuma ◽

◽

T. Christy Bobby ◽

S. Malathi ◽

◽

...

Keyword(s):

Emotion Recognition ◽

Recognition Rate ◽

Principal Component ◽

Well Being ◽

Recognition System ◽

Autism Spectrum ◽

Learning Networks ◽

Human Communication ◽

Feature Level Fusion ◽

Level Fusion

Emotion recognition is important in human communication and to achieve a complete interaction between humans and machines. In medical applications, emotion recognition is used to assist the children with Autism Spectrum Disorder (ASD to improve their socio-emotional communication, helps doctors with diagnosis of diseases such as depression and dementia and also helps the caretakers of older patients to monitor their well-being. This paper discusses the application of feature level fusion of speech and facial expressions of different emotions such as neutral, happy, sad, angry, surprise, fearful and disgust. Also, to explore how best to build the deep learning networks to classify the emotions independently and jointly from these two modalities. VGG-model is utilized to extract features from facial images, and spectral features are extracted from speech signals. Further, feature level fusion technique is adopted to fuse the features extracted from the two modalities. Principal Component Analysis (PCA is implemented to choose the significant features. The proposed method achieved a maximum score of 90% on training set and 82% on validation set. The recognition rate in case of multimodal data improved greatly when compared to unimodal system. The multimodal system gave an improvement of 9% compared to the performance of the system based on speech. Thus, result shows that the proposed Multimodal Emotion Recognition (MER outperform the unimodal emotion recognition system.

Download Full-text

Improved Gender Recognition during Stepping Activity for Rehab Application Using the Combinatorial Fusion Approach of EMG and HRV

10.20944/preprints201703.0206.v1 ◽

2017 ◽

Author(s):

Nor Aziyatul Izni Mohd Rosli ◽

Mohd Azizi Abdul Rahman ◽

Malarvili Balakrishnan ◽

Takashi Komeda ◽

Saiful Amri Mazlan ◽

...

Keyword(s):

Feature Selection ◽

Nearest Neighbor ◽

Recognition Rate ◽

Support Vector ◽

Gender Classification ◽

Gender Recognition ◽

K Nearest Neighbor ◽

Combinatorial Fusion ◽

Feature Selection Approach ◽

Fusion Approach

Gender recognition is trivial for physiotherapist, but it is considered a challenge for computers. The electromyography (EMG) and heart rate variability (HRV) were utilized in this work for gender recognition during the stepping exercise using a stepper. The relevant features were extracted and selected. The selected features were then fused to automatically predict gender recognition. However, the feature selection for gender classification became a challenge to ensure better accuracy. Thus, in this paper, a feature selection approach based on both the performance and the diversity between the two features from the rank-score characteristic (RSC) function in a combinatorial fusion approach (CFA) was employed. Then, the features from the selected feature sets were fused using a CFA. The results were then compared with other fusion techniques such as naive bayes (NB), decision tree (J48), k-nearest neighbor (KNN) and support vector machine (SMO). Besides, the results were also compared with previous researches in gender recognition. The experimental results showed that the CFA was efficient for feature selection. The fusion method was also able to improve the accuracy of the gender recognition rate. The CFA provides much better gender classification results which is 94.51% compared to Nazarloo's work (90.34%) and other classifiers.

Download Full-text

Image classification method of cashmere and wool based on the multi-feature selection and random forest method

Textile Research Journal ◽

10.1177/00405175211046060 ◽

2021 ◽

pp. 004051752110460

Author(s):

Yaolin Zhu ◽

Jiameng Duan ◽

Yunhong Li ◽

Tong Wu

Keyword(s):

Feature Selection ◽

Random Forest ◽

Textile Industry ◽

Texture Feature ◽

Principal Component ◽

Weight Coefficient ◽

Data Set ◽

Random Forest Method ◽

Independent Features ◽

Occurrence Matrix

Cashmere and wool play an important role in the wool industry and textile industry, and suitable features are the key to identifying them. To obtain effective features and improve the accuracy of cashmere and wool classification, the multi-feature selection and random forest method is used to express in this article. Firstly, the gray-gradient co-occurrence matrix model is used for texture feature extraction to construct the original high-dimensional feature data set; secondly, considering that the original feature data set contains a large number of invalid and redundant features, the feature selection algorithm combining correlation analysis and principal component analysis–weight coefficient evaluation is used to obtain important features, independent features, and principal component sensitive features to complement each other; last but not least, the optimized random forest model analyzes the results. The results show that the combination of multi-feature selection subsets and random forest makes the classification accuracy of cashmere and wool more reliable, and the accuracy fluctuates around 90%.

Download Full-text

Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets

Algorithms ◽

10.3390/a15010021 ◽

2022 ◽

Vol 15 (1) ◽

pp. 21

Author(s):

Consolata Gakii ◽

Paul O. Mireji ◽

Richard Rimiru

Keyword(s):

Feature Selection ◽

Association Rule ◽

Association Rule Mining ◽

Principal Component ◽

Recursive Feature Elimination ◽

High Dimensional ◽

Rule Mining ◽

Rnaseq Data ◽

Feature Selection Approach ◽

Feature Selection Techniques

Analysis of high-dimensional data, with more features () than observations () (), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

Download Full-text