scholarly journals A Novel Dictionary Learning Model with PT-HLBP for Palmprint Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Xiumei Guo ◽  
Weidong Zhou
Keyword(s):  
Feature Extraction ◽  
Hong Kong ◽  
Dictionary Learning ◽  
Time Complexity ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Experimental Results ◽  
Palmprint Recognition ◽  
Image Rotation ◽  
Testing Phase

A novel projective dictionary pair learning (PDPL) model with statistical local features for palmprint recognition is proposed. Pooling technique is used to enhance the invariance of hierarchical local binary pattern (PT-HLBP) for palmprint feature extraction. PDPL is employed to learn an analysis dictionary and a synthesis dictionary which are utilized for image discrimination and representation. The proposed algorithm has been tested by the Hong Kong Polytechnic University (PolyU) database (v2) and ideal recognition accuracy can be achieved. Experimental results indicate that the algorithm not only greatly reduces the time complexity in training and testing phase, but also exhibits good robustness for image rotation and corrosion.

Wear Particles Recognition Based on Improved LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1874-1878
Author(s):  
Guo De Wang ◽  
Zhi Sheng Jing ◽  
Guo Wei Qin ◽  
Shan Chao Tu
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Binary Sequence ◽  
Texture Feature ◽  
Experimental Results ◽  
Wear Particles ◽  
Texture Feature Extraction ◽  
Feature Extraction Method

Wear particles recognition is a key link in the process of Ferrography analysis. Different kinds of wear particles vary greatly in texture, texture feature is one of the most important feature in wear particles recognition. Local Binary Pattern (LBP) is an efficient operator for texture description. The binary sequence of traditional LBP operator is obtained by the comparison between the gray value of the neighborhood and the gray value of the center pixel of the neighborhood, the comparison is too simple to cause the loss of the texture. In this paper, an improved LBP operator is presented for texture feature extraction and it is applied to the recognition of severe sliding particles, fatigue spall particles and laminar particles. The experimental results show that our method is an effective feature extraction method and obtains better recognition accuracy compared with other methods.

scholarly journals Face Recognition Based on Viola-Jones Face Detection Method and Local Binary Pattern (LBP)

2019 ◽  
pp. 50-55
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Detection Method ◽  
Histogram Equalization ◽  
Face Image ◽  
Accuracy Rate ◽  
Data Set ◽  
Testing Phase

In this paper, the system consists of many steps, the first step includes the histogram equalization, detection, feature extraction, and classification. At first, the data set of a face image is segmented into four segments, after that Local Binary Pattern (LBP) algorithm is performed to extract features for each segment. The best feature vectors for all persons are stored in a new dataset in the next stage in order to be used in the testing phase. Finally, the accuracy rate of performance is evaluated to prove its robustness. Experiments show satisfying results and more accuracy achieved by the paper.

Palmprint Recognition Using Fusion of Local Binary Pattern and Histogram of Oriented Gradients

2020 ◽  
pp. 22-31
Author(s):  
Hardik Agarwal ◽  
◽  
Kanika Somani ◽  
Shivangi Sharma ◽  
Prerna Arora ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Palmprint Recognition ◽  
Histogram Of Oriented Gradients ◽  
Extraction Techniques ◽  
Classification Techniques ◽  
Segmented Image ◽  
Average Accuracy

In this paper, unique features of the segmented image samples are extracted by using two major feature extraction techniques: Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG). After this, these features are fused to get more precise and productive outcomes. The average accuracy of the three distinct datasets that were generated using the LBP and HOG features are determined. To calculate the accuracy of the three distinct models, classification techniques like KNN and SVM, are adopted.

A Multi-Feature Convolution Neural Network for Automatic Flower Recognition

2021 ◽  
pp. 2150281
Author(s):  
Juan Ran ◽  
Yu Shi ◽  
Jinhao Yu ◽  
Delong Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Texture Features ◽  
Activation Function ◽  
Multiple Features ◽  
Color Features ◽  
Definition Of ◽  
Texture Extraction

This paper discusses how to efficiently recognize flowers based on a convolutional neural network (CNN) using multiple features. Our proposed work consists of three phases including segmentation by Otsu thresholding with particle swarm optimization algorithms, feature extraction of color, shape, texture and recognition with the LeNet-5 neural network. In the feature extraction, an improved H component with the definition of WGB value is applied to extract the color feature, and a new algorithm based on local binary pattern (LBP) is proposed to enhance the accuracy of texture extraction. Besides this, we replace ReLU with Mish as activation function in the network design, and therefore increase the accuracy by 8% accuracy according to our comparison. The Oxford-102 and Oxford-17 datasets are adopted for benchmarking. The experimental results show that the combination of color features and texture features generates the highest recognition accuracy as 92.56% on Oxford-102 and 93% on Oxford-17.

A Novel BCI Classifier Based on Autoregressive Model and Support Vector Machine

2013 ◽  
Vol 694-697 ◽  
pp. 2522-2525
Author(s):  
Lu Huang ◽  
Jun Gu ◽  
Ran Li ◽  
Xiang Jun Li ◽  
Hong Yu
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Autoregressive Model ◽  
Recognition Accuracy ◽  
Experimental Results ◽  
Ar Model ◽  
Support Vector ◽  
Eeg Signals ◽  
Bci System

The efficient featureextraction and classification are very crucial for brain computerinterface(BCI) system. In this paper, feature extraction and classification forP300, a kind of EEG characteristic potential, was conducted. Afterpreprocessing EEG signals, we used autoregressive(AR) model for featureextraction, segmenting the selected EEG channel data and building AR model foreach segment respectively. AR model coefficients were estimated by using leastsquare method, and the estimated coefficient sequence constituted the featurevector. We applied support vector machine(SVM) for classification andexperimented on real EEG dataset. The experimental results showed the proposedmethod had a good recognition accuracy, being worth researching in the field of BCI.

OFFLINE YORÙBÁ HANDWRITTEN WORD RECOGNITION USING GEOMETRIC FEATURE EXTRACTION AND SUPPORT VECTOR MACHINE CLASSIFIER

2020 ◽  
Vol 5 (2) ◽  
pp. 504
Author(s):  
Matthias Omotayo Oladele ◽  
Temilola Morufat Adepoju ◽  
Olaide ` Abiodun Olatoke ◽  
Oluwaseun Adewale Ojo
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Word Recognition ◽  
Support Vector Machine Classifier ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Support Vector ◽  
Geometric Features ◽  
Total Length ◽  
Yoruba Language

Yorùbá language is one of the three main languages that is been spoken in Nigeria. It is a tonal language that carries an accent on the vowel alphabets. There are twenty-five (25) alphabets in Yorùbá language with one of the alphabets a digraph (GB). Due to the difficulty in typing handwritten Yorùbá documents, there is a need to develop a handwritten recognition system that can convert the handwritten texts to digital format. This study discusses the offline Yorùbá handwritten word recognition system (OYHWR) that recognizes Yorùbá uppercase alphabets. Handwritten characters and words were obtained from different writers using the paint application and M708 graphics tablets. The characters were used for training and the words were used for testing. Pre-processing was done on the images and the geometric features of the images were extracted using zoning and gradient-based feature extraction. Geometric features are the different line types that form a particular character such as the vertical, horizontal, and diagonal lines. The geometric features used are the number of horizontal lines, number of vertical lines, number of right diagonal lines, number of left diagonal lines, total length of all horizontal lines, total length of all vertical lines, total length of all right slanting lines, total length of all left-slanting lines and the area of the skeleton. The characters are divided into 9 zones and gradient feature extraction was used to extract the horizontal and vertical components and geometric features in each zone. The words were fed into the support vector machine classifier and the performance was evaluated based on recognition accuracy. Support vector machine is a two-class classifier, hence a multiclass SVM classifier least square support vector machine (LSSVM) was used for word recognition. The one vs one strategy and RBF kernel were used and the recognition accuracy obtained from the tested words ranges between 66.7%, 83.3%, 85.7%, 87.5%, and 100%. The low recognition rate for some of the words could be as a result of the similarity in the extracted features.

scholarly journals Privacy Leakage in Mobile Sensing: Your Unlock Passwords Can Be Leaked through Wireless Hotspot Functionality

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhang ◽  
Xiaolong Zheng ◽  
Zhanyong Tang ◽  
Tianzhang Xing ◽  
Xiaojiang Chen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Mobile Sensing ◽  
Experimental Results ◽  
Smart Devices ◽  
Detection Accuracy ◽  
Phase Information ◽  
Current Sensing ◽  
Privacy Leakage ◽  
Sensing Systems ◽  
Additional Hardware

Mobile sensing has become a new style of applications and most of the smart devices are equipped with varieties of sensors or functionalities to enhance sensing capabilities. Current sensing systems concentrate on how to enhance sensing capabilities; however, the sensors or functionalities may lead to the leakage of users’ privacy. In this paper, we present WiPass, a way to leverage the wireless hotspot functionality on the smart devices to snoop the unlock passwords/patterns without the support of additional hardware. The attacker can “see” your unlock passwords/patterns even one meter away. WiPass leverages the impacts of finger motions on the wireless signals during the unlocking period to analyze the passwords/patterns. To practically implement WiPass, we are facing the difficult feature extraction and complex unlock passwords matching, making the analysis of the finger motions challenging. To conquer the challenges, we use DCASW to extract feature and hierarchical DTW to do unlock passwords matching. Besides, the combination of amplitude and phase information is used to accurately recognize the passwords/patterns. We implement a prototype of WiPass and evaluate its performance under various environments. The experimental results show that WiPass achieves the detection accuracy of 85.6% and 74.7% for passwords/patterns detection in LOS and in NLOS scenarios, respectively.

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