A Novel Feature Extraction Based Person Recognition Using Local Phase Quantization and Geometric Features

2021 ◽  
Author(s):  
P Karuppanan ◽  
K. Dhanalakshmi
Keyword(s):  
Feature Extraction ◽  
High Performance ◽  
Performance Metrics ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Video Frame ◽  
Facial Features ◽  
Local Phase Quantization ◽  
Working Principle ◽  
Phase Quantization

Abstract At present, it is simple for everyone to generate digital pictures of their routine life and use them for different purposes. Similarly, facial recognition is a trending technology that can identify or verify an individual from a video frame or digital image from any source. There are numerous techniques involved in the working principle of facial recognition. But the simplified method is feature extraction by comparing the particular facial features of the images from the collected dataset. Multiple algorithms are existing for feature extraction, but they fail to give high accuracy. The proposed algorithm based on deep learning provides a high recognition rate by using a convolutional neural network for classification. For feature extraction, Local Phase quantization, Geometric-based features, and directional graph-based methods are implemented. Various performance metrics, such as recognition rate, classification accuracy, accuracy, precision, recall, F1-score is evaluated. The proposed method achieves high-performance values when it is compared with other existing methods. It is mainly developed to calculate the casual visit of a person to the mall, and it is also deployed for criminal identification.

Kernel Null Space Marginal Fisher Analysis for Face Recognition

2014 ◽  
Vol 889-890 ◽  
pp. 1065-1068
Author(s):  
Yu’e Lin ◽  
Xing Zhu Liang ◽  
Hua Ping Zhou
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Sample Size ◽  
High Performance ◽  
Null Space ◽  
Small Sample Size ◽  
Recognition Rate ◽  
Original Data ◽  
Small Sample ◽  
Neighborhood Structure

In the recent years, the feature extraction algorithms based on manifold learning, which attempt to project the original data into a lower dimensional feature space by preserving the local neighborhood structure, have drawn much attention. Among them, the Marginal Fisher Analysis (MFA) achieved high performance for face recognition. However, MFA suffers from the small sample size problems and is still a linear technique. This paper develops a new nonlinear feature extraction algorithm, called Kernel Null Space Marginal Fisher Analysis (KNSMFA). KNSMFA based on a new optimization criterion is presented, which means that all the discriminant vectors can be calculated in the null space of the within-class scatter. KNSMFA not only exploits the nonlinear features but also overcomes the small sample size problems. Experimental results on ORL database indicate that the proposed method achieves higher recognition rate than the MFA method and some existing kernel feature extraction algorithms.

scholarly journals Research on Face Recognition Algorithm Based on Multi-Class Support Vector Machine

2021 ◽  
Vol 2078 (1) ◽  
pp. 012049
Author(s):  
Liuxun Xue ◽  
Hui Li ◽  
Ping Wang ◽  
Zhiyang Lin ◽  
Huanyu Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Face Recognition ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Machine Method ◽  
Feature Extraction Method ◽  
Main Research ◽  
Face Images

Abstract Facial recognition is one of the main research directions in the field of artificial intelligence and image processing. It has been widely used in identity authentication, video surveillance and biological detection. Because it is non-contact, natural, convenient and reliable, facial recognition has become a popular choice for biometric systems. The accuracy of facial recognition still needs to be improved, the main goal of this paper is to improve the accuracy of face recognition. Based on the support vector machine method, the focus is on the feature extraction and feature matching of face images. In view of the particularity of face images, the pre-processing of face images is studied. In this paper, grayscale normalization and geometric normalization pre-processing methods are used. In order to reduce the interference factors of the image as much as possible, the features are high-lighted, and the non-featured parts are weakened, this paper adopts the Histogram of Oriented Gradient feature extraction method. Then we proposed a new method based on SVM, which uses a one-to-many method to construct multiple SVM classifiers, selects the optimal parameters through repeated experiments, and selects ORL face database for testing. The recognition rate can reach about 98.5%.

Research on Face Recognition Technology of Real-Time Video High Performance Similarity

2015 ◽  
Vol 713-715 ◽  
pp. 2160-2164
Author(s):  
Zhao Nan Yang ◽  
Shu Zhang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Real Time ◽  
High Performance ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Recognition System ◽  
Image Preprocessing ◽  
Face Images ◽  
Face Recognition System

A new similarity measurement standard is proposed, namely background similarity matching. Learning algorithm based on kernel function is utilized in the method for feature extraction and classification of face image. Meanwhile, a real-time video face recognition method is proposed, image binary algorithm in similarity calculation is introduced, and a video face recognition system is designed and implemented [1-2]. The system is provided with a camera to obtain face images, and face recognition is realized through image preprocessing, face detection and positioning, feature extraction, feature learning and matching. Design, image preprocessing, feature positioning and extraction, face recognition and other major technologies of face recognition systems are introduced in details. Lookup mode from top down is improved, thereby improving lookup accuracy and speed [3-4]. The experimental results showed that the method has high recognition rate. Higher recognition rate still can be obtained even for limited change images of face images and face gesture with slightly uneven illumination. Meanwhile, training speed and recognition speed of the method are very fast, thereby fully meeting real-time requirements of face recognition system [5]. The system has certain face recognition function and can well recognize front faces.

A Study of Different Methodologies Helpful in the Identification of Offline Handwritten Script

2018 ◽  
Vol 6 (6) ◽  
pp. 307
Author(s):  
Manish M. Kayasth ◽  
Bharat C. Patel
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Post Processing ◽  
Classification Technique ◽  
Scanned Image ◽  
Gujarati Language ◽  
High Degree ◽  
Selection Of

The entire character recognition system is logically characterized into different sections like Scanning, Pre-processing, Classification, Processing, and Post-processing. In the targeted system, the scanned image is first passed through pre-processing modules then feature extraction, classification in order to achieve a high recognition rate. This paper describes mainly on Feature extraction and Classification technique. These are the methodologies which play an important role to identify offline handwritten characters specifically in Gujarati language. Feature extraction provides methods with the help of which characters can identify uniquely and with high degree of accuracy. Feature extraction helps to find the shape contained in the pattern. Several techniques are available for feature extraction and classification, however the selection of an appropriate technique based on its input decides the degree of accuracy of recognition. 

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

Multimodal biometric system using deep learning based on face and finger vein fusion

2021 ◽  
pp. 1-13
Author(s):  
Shikhar Tyagi ◽  
Bhavya Chawla ◽  
Rupav Jain ◽  
Smriti Srivastava
Keyword(s):  
High Performance ◽  
Recognition Accuracy ◽  
Error Rates ◽  
Facial Features ◽  
Biometric System ◽  
Deep Convolutional Neural Networks ◽  
Finger Vein ◽  
Biometric Systems ◽  
Overall Performance ◽  
Recognition Systems

Single biometric modalities like facial features and vein patterns despite being reliable characteristics show limitations that restrict them from offering high performance and robustness. Multimodal biometric systems have gained interest due to their ability to overcome the inherent limitations of the underlying single biometric modalities and generally have been shown to improve the overall performance for identification and recognition purposes. This paper proposes highly accurate and robust multimodal biometric identification as well as recognition systems based on fusion of face and finger vein modalities. The feature extraction for both face and finger vein is carried out by exploiting deep convolutional neural networks. The fusion process involves combining the extracted relevant features from the two modalities at score level. The experimental results over all considered public databases show a significant improvement in terms of identification and recognition accuracy as well as equal error rates.

scholarly journals Fuzzy enhancement and deep hash layer based neural network to detect Covid-19

2021 ◽  
pp. 1-11
Author(s):  
Amita Nandal ◽  
Marija Blagojevic ◽  
Danijela Milosevic ◽  
Arvind Dhaka ◽  
Lakshmi Narayan Mishra
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Performance Metrics ◽  
Regularization Parameter ◽  
Fuzzy Membership Function ◽  
Kappa Statistics ◽  
Pixel Intensity ◽  
Testing Sample ◽  
X Ray ◽  
Conventional Methods

This paper proposes a deep learning framework for Covid-19 detection by using chest X-ray images. The proposed method first enhances the image by using fuzzy logic which improvises the pixel intensity and suppresses background noise. This improvement enhances the X-ray image quality which is generally not performed in conventional methods. The pre-processing image enhancement is achieved by modeling the fuzzy membership function in terms of intensity and noise threshold. After this enhancement we use a block based method which divides the image into smooth and detailed regions which forms a feature set for feature extraction. After feature extraction we insert a hashing layer after fully connected layer in the neural network. This hash layer is advantageous in terms of improving the overall accuracy by computing the feature distances effectively. We have used a regularization parameter which minimizes the feature distance between similar samples and maximizes the feature distance between dissimilar samples. Finally, classification is done for detection of Covid-19 infection. The simulation results present a comparison of proposed model with existing methods in terms of some well-known performance indices. Various performance metrics have been analysed such as Overall Accuracy, F-measure, specificity, sensitivity and kappa statistics with values 93.53%, 93.23%, 92.74%, 92.02% and 88.70% respectively for 20:80 training to testing sample ratios; 93.84%, 93.53%, 93.04%, 92.33%, and 91.01% respectively for 50:50 training to testing sample ratios; 95.68%, 95.37%, 94.87%, 94.14%, and 90.74% respectively for 80:20 training to testing sample ratios have been obtained using proposed method and it is observed that the results using proposed method are promising as compared to the conventional methods.

Research on Feature Extraction and Optimization of Cashmere and Wool Fiber Based on Digital Image

2014 ◽  
Vol 667 ◽  
pp. 260-263 ◽  
Author(s):  
Heng Chen ◽  
Ya Xia Liu
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Textile Industry ◽  
Recognition Rate ◽  
Wool Fiber

The identification of cashmere and wool fiber is one of the difficult problems in the textile industry. Three features including diameter, diameter shaft parameter and the density are extracted using MATLAB.Support vector machine is used to recognition the cashmere and wool fiber. The experiment shows that diameter is not a useful feature for recognition and density combined diameter shaft parameter is useful for differentiate them and recognition rate is 87.35%.

scholarly journals A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chenchen Huang ◽  
Wei Gong ◽  
Wenlong Fu ◽  
Dongyu Feng
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Original Method ◽  
Speech Emotion Recognition ◽  
High Dimensional ◽  
Svm Classifier ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

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