Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) for hyperspectral image based face recognition in real time door locking applications

2021 ◽  
pp. 1-11
Author(s):  
Ashok Kumar Rai ◽  
Radha Senthilkumar ◽  
A. Kanan
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recurrent Neural Network ◽  
Hyperspectral Image ◽  
Selection Process ◽  
Facial Feature ◽  
Recognition System ◽  
Training Data ◽  
Search Optimization ◽  
Feature Extraction And Selection

Face recognition is one of the best applications of computer recognition and recent smart house applications. Therefore, it draws considerable attention from researchers. Several face recognition algorithms have been proposed in the last decade, but these methods did not give the efficient outcome. Therefore, this work introduces a novel constructive training algorithm for smart face recognition in door locking applications. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization (FRNN-MDSO) Strategy is applied to face recognition application. The steady preparing system has been utilized where the training designs are adapted steadily and are divided into completely different modules. The facial feature process works on global and local features. After the feature extraction and selection process, employ the improved classifier followed by the Framed Recurrent Neural Network classification technique. Finally, the face image based on the feature library can be identified. The proposed Framed Recurrent Neural Network with Mutated Dragonfly Search Optimization starts with a single training pattern using Bidirectional Encoder Representations from Transformers (BERT) model. During network training, the Training Data (TD) decrease the Mean Square Error (MSE) while the matching process increases the algorithms generated which are trapped at the local minimum. The training data have been trained to increase the number of input forms (one after the other) until all the forms are selected and trained. An FRNN-MDSO based face recognition system is built, and face recognition is tested using hyperspectral Database parameters. The simulation results indicate that the proposed method acquires the associate grade optimum design of FRNN with MDSO methodology using the present constructive algorithm and prove the proposed FRNN-MDSO method’s effectiveness compared to the conventional architecture methods.

The Comparison of Laplacianfaces QR Decomposition and Linear Discriminant Analysis QR Decomposition Algorithm for Face Recognition System on Orthogonal Subspace

2014 ◽  
Vol 71 (1) ◽  
Author(s):  
Purbandini Purbandini
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Selection Process ◽  
Recognition System ◽  
Qr Decomposition ◽  
Training Data ◽  
Distance Method ◽  
Linear Discriminant ◽  
Face Recognition System

Development of an optimal face recognition system will greatly depend on the characteristics of the selection process are as a basis to pattern recognition. In the characteristic selection process, there are 2 aspects that will be of mutual influence such the reduction of the amount of data used in the classification aspects and increasing discrimination ability aspects. Linear Discriminat Analysis method helps presenting the global structure while Laplacianfaces method is one method that is based on appearance (appearance-based method) in face recognition, in which the local manifold structure presented in the adjacency graph mapped from the training data points. Linear Discriminant Analysis QR decomposition has a computationally low cost because it has small dimensions so that the efficiency and scalability are very high when compared with algorithms of other Linear Discriminant Analysis methods. Laplacianfaces QR decomposition was a algorithm to obtain highly speed and accuracy, and tiny space to keep data on the face recognition. This algorithm consists of 2 stages. The first stage maximizes the distance of between-class scatter matrices by using QR decomposition and the second stage to minimize the distance of within-class scatter matrices. Therefore, it is obtained an optimal discriminant in the data. In this research, classification using the Euclidean distance method. In these experiments using face databases of the Olivetti-Att-ORL, Bern and Yale. The minimum error was achieved with the Laplacianfaces QR decomposition and Linear Discriminant Analysis QR decomposition are 5.88% and 9.08% respectively. 

scholarly journals Transfer learning using AlexNet Convolutional Neural Network for Face Recognition

2020 ◽  
Vol 9 (11) ◽  
pp. 285-294
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Recognition System ◽  
Training Data ◽  
Fine Tuning ◽  
Data Sets ◽  
Learning Method ◽  
Face Recognition System

This research is aimed to achieve high-precision accuracy and for face recognition system. Convolution Neural Network is one of the Deep Learning approaches and has demonstrated excellent performance in many fields, including image recognition of a large amount of training data (such as ImageNet). In fact, hardware limitations and insufficient training data-sets are the challenges of getting high performance. Therefore, in this work the Deep Transfer Learning method using AlexNet pre-trained CNN is proposed to improve the performance of the face-recognition system even for a smaller number of images. The transfer learning method is used to fine-tuning on the last layer of AlexNet CNN model for new classification tasks. The data augmentation (DA) technique also proposed to minimize the over-fitting problem during Deep transfer learning training and to improve accuracy. The results proved the improvement in over-fitting and in performance after using the data augmentation technique. All the experiments were tested on UTeMFD, GTFD, and CASIA-Face V5 small data-sets. As a result, the proposed system achieved a high accuracy as 100% on UTeMFD, 96.67% on GTFD, and 95.60% on CASIA-Face V5 in less than 0.05 seconds of recognition time.

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

scholarly journals Improved Empirical Identification of the Inverse Model of a Squeeze-Film Damper Bearing Based on a Recurrent Neural Network

2018 ◽  
Author(s):  
Ghaith Ghanim Al-Ghazal ◽  
Philip Bonello ◽  
Sergio G. Torres Cedillo
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Structural Model ◽  
Solution Process ◽  
Time History ◽  
Inverse Model ◽  
Training Data ◽  
Squeeze Film ◽  
Squeeze Film Damper ◽  
History Of

Most recently proposed techniques for inverse rotordynamic problems seek to identify the unbalance on a rotor using a known structural model and measurements from externally mounted sensors only. Such non-intrusive techniques are important for balancing rotors that cannot be accessed under operational conditions because of temperature or space restrictions. The presence of nonlinear bearings, like squeeze-film damper (SFD) bearings used in aero-engines, complicates the solution process of the inverse rotordynamic problem. In certain practical aero-engine configurations, the solution process requires a substitute for internal instrumentation to quantify the SFD journal vibration. This can be provided by an inverse model of the SFD bearing which outputs the time history of the relative vibration of the SFD journal relative to its housing, for a given input time history of the SFD force. This paper focuses on the inverse model of the SFD and presents an improved methodology for its identification via a Recurrent Neural Network (RNN) trained using experimental data from a purposely designed rig. The novel application of chirp excitation via two orthogonal shakers considerably improves both the quality of the training data and the efficiency of its generation, relative to an earlier preliminary work. Validation test results show that the RNNs can predict the journal displacement time history with reasonable accuracy. It is therefore expected that such an inverse SFD model would serve as a reliable component in the solution of the wider inverse problem of a rotordynamic system.

Deep Learning Based Adaptive Recurrent Neural Network for Detection of Myocardial Infarction

2021 ◽  
Vol 11 (12) ◽  
pp. 3044-3053
Author(s):  
Rakesh Kumar Mahendran ◽  
V. Prabhu ◽  
V. Parthasarathy ◽  
A. Mary Judith
Keyword(s):  
Neural Network ◽  
Myocardial Infarction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Training Data ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Detection Techniques

Myocardial infarction (MI) may precipitate severe health damage and lead to irreversible death of the heart muscle, the result of prolonged lack of oxygen if it is not treated in a timely manner. Lack of accurate and early detection techniques for this heart disease has reduced the efficiency of MI diagnosis. In this paper, the design, and implementation of an efficient deep learning algorithm called Adaptive Recurrent neural network (ARNN) is proposed for the MI detection. The main objective of the proposed work is the accurate identification of MI disease using ECG signals. ECG signal denoising has been performed using the Multi-Notch filter, which removes the specified noise frequency range. Discrete wavelet transform (DWT) is utilized for performing the feature extraction that decomposes the ECG signal into varied scales with waveletfiltering bank. After the extraction of specific QRS features, classification of the defected and normal ECG arrhythmic beat has been performed using the deep learning-based ARNN classifier. The MIT-BIH database has been used for testing and training data. The performance of the proposed algorithm is evaluated based on classification accuracy. Results that are attained include the classification accuracy of about 99.21%, 99% of sensitivity and 99.4% of specificity with PPV and NPV of about 99.4 and 99.01 values indicate the enhanced performance of our proposed work compared with the conventional LSTM-CAE and LSTM-CNN techniques.

A Lyapunov Theory-Based Neural Network Approach for Face Recognition

2010 ◽  
pp. 23-48 ◽  
Author(s):  
Li-Minn Ang ◽  
King Hann Lim ◽  
Kah Phooi Seng ◽  
Siew Wen Chin
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Principal Component ◽  
Lyapunov Stability Theory ◽  
Recognition System ◽  
Lyapunov Theory ◽  
Neural Network Approach ◽  
Linear Discriminant ◽  
Feature Based ◽  
Definition Of

This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher’s Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems.

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