Automatic Face Emotion Recognition With the Aid of Probability-Based Bird Swarm-Trained Neural Network

2021 ◽  
Vol 12 (4) ◽  
pp. 1-24
Author(s):  
Bhagyashri Devi ◽  
M. Mary Synthuja Jain Preetha
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Face Detection ◽  
Orthogonal Transformation ◽  
Principal Component ◽  
Discrete Wavelet ◽  
The Face ◽  
Bird Swarm Algorithm ◽  
Trained Neural Network ◽  
Face Emotion Recognition

This paper intends to develop a novel FER model, which consists of four stages: (1) face detection, (2) feature extraction, (3) dimension reduction, and (4) classification. In this context, the face detection is done using Viola Jones method (VJ). It is the first object recognition model to offer better recognition rates in real-time. Further, features extraction techniques like local binary pattern (LBP) and discrete wavelet transform (DWT) are used for extracting the features from face detected images. Moreover, the dimension reduction of features is done using principal component analysis (PCA), which is an arithmetical process that exploits an orthogonal transformation to exchange a group of annotations of probably interrelated constraints. The classification procedure is performed using neural network (NN), with the new training algorithm called bird swarm algorithm, which is modified based on probability and hence termed as probability-based BSA (P-BSA).

scholarly journals Implementing Neural Network and Multi resolution analysis in EEG signal for early detection of epilepsy

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Sachin Shrestha ◽  
Rupesh Dahi Shrestha ◽  
Bhojraj Thapa
Keyword(s):  
Neural Network ◽  
Dimension Reduction ◽  
Epileptic Seizure ◽  
Principal Component ◽  
Epileptic Seizures ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Multi Resolution Analysis ◽  
Resolution Analysis

Epilepsy is a neurological disorder of brain and the electroencephalogram (EEG) signals are commonly used to detect the epileptic seizures, the result of abnormal electrical activity in the brain. This paper focuses on the analysis of EEG signal to detect the presence of the epileptic seizure prior to its occurrence. The result could aid the individual in the initiation of delay sensitive diagnostic, therapeutic and alerting procedures. The methodology involves the multi resolution analysis (MRA) of the EEG signals of epileptic patient. MRA is carried out using discrete wavelet transform with daubechies 8 as the mother wavelet. For EEG data, the database of MIT-BIH of seven patients with different cases of epileptic seizure was used. The result with one of the patients showed presence of a unique pattern during the spectral analysis of the signal over different bands. Hence, based on the first patient, similar region is selected with the other patients and the multi-resolution analysis along with the principal component analysis (PCA) for the dimension reduction is carried out. Finally, these are treated with neural network to perform the classification of the signal either the epilepsy is occurring or not. The final results showed 100% accuracy with the detection with the neural network however it uses a large amount of data for analysis. Thus, the same was tested with dimension reduction using PCA which reduced the average accuracy to 89.51%. All the results have been simulated within the Matlab environment.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Archana Harsing Sable ◽  
Sanjay N. Talbar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Plastic Surgery ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Traditional Learning ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Orientation Histogram ◽  
The Face

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

Automated Multichannel Signal Classification Systems for Ultrasonic Nondestrucitve Evaluation

2006 ◽  
Vol 321-323 ◽  
pp. 1266-1269 ◽  
Author(s):  
J. Kim ◽  
P. Ramuhalli ◽  
L. Udpa ◽  
S. Udpa
Keyword(s):  
Neural Network ◽  
Spatial Correlation ◽  
Principal Component ◽  
Automated Analysis ◽  
Classification Systems ◽  
Inspection Systems ◽  
Weld Inspection ◽  
Correlation Information ◽  
Trained Neural Network ◽  
Analysis System

A key requirement in most ultrasonic weld inspection systems is the ability for rapid automated analysis to identify the type of flaw. Incorporation of spatial correlation information from adjacent A-scans can improve performance of the analysis system. This paper describes two neural network based classification techniques that use correlation of adjacent A-scans. The first method relies on differences in individual A-scans to classify signals using a trained neural network, with a post-processing mechanism to incorporate spatial correlation information. The second technique transforms a group of spatially localized signals using a 2-dimensional transform, and principal component analysis is applied to the transform coefficients to generate a reduced dimensional feature vectors for classification. Results of applying the proposed techniques to data obtained from weld inspection are presented, and the performances of the two approaches are compared.

scholarly journals Robot movement controller based on dynamic facial pattern recognition

2021 ◽  
Vol 22 (2) ◽  
pp. 733
Author(s):  
Siti Nurmaini ◽  
Ahmad Zarkasi ◽  
Deris Stiawan ◽  
Bhakti Yudho Suprapto ◽  
Sri Desy Siswanti ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Face Detection ◽  
Detection System ◽  
Accuracy Rate ◽  
True Value ◽  
The Face ◽  
Facial Pattern ◽  
Lower Frame ◽  
The Right

In terms of movement, mobile robots are equipped with various navigation techniques. One of the navigation techniques used is facial pattern recognition. But Mobile robot hardware usually uses embedded platforms which have limited resources. In this study, a new navigation technique is proposed by combining a face detection system with a ram-based artificial neural network. This technique will divide the face detection area into five frame areas, namely top, bottom, right, left, and neutral. In this technique, the face detection area is divided into five frame areas, namely top, bottom, right, left, and neutral. The value of each detection area will be grouped into the ram discriminator. Then a training and testing process will be carried out to determine which detection value is closest to the true value, which value will be compared with the output value in the output pattern so that the winning discriminator is obtained which is used as the navigation value. In testing 63 face samples for the Upper and Lower frame areas, resulting in an accuracy rate of 95%, then for the Right and Left frame areas, the resulting accuracy rate is 93%. In the process of testing the ram-based neural network algorithm pattern, the efficiency of memory capacity in ram, the discriminator is 50%, assuming a 16-bit input pattern to 8 bits. While the execution time of the input vector until the winner of the class is under milliseconds (ms).

scholarly journals Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Louis Asiedu ◽  
Bernard O. Essah ◽  
Samuel Iddi ◽  
K. Doku-Amponsah ◽  
Felix O. Mettle
Keyword(s):  
Principal Component ◽  
Bilateral Symmetry ◽  
Recognition Algorithm ◽  
Face Image ◽  
Discrete Wavelet ◽  
Partial Occlusion ◽  
Face Images ◽  
Numerical Assessment ◽  
The Face ◽  
Value Decomposition

The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.

CARDIAC ADVANCED ARRYTHMIA PREDICTION SYSTEMS- CAAPS

2021 ◽  
pp. 54-55
Author(s):  
Pradeep Kumar Radhakrishnan ◽  
Gayathri Ananyajyothi Ambat ◽  
Saihrudya Samhita ◽  
Murugan U S ◽  
Tarig Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Ventricular Arrhythmias ◽  
Deep Neural Network ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Term Memory

There is a constant search for novel methods of classication and predicting cardiac rhythm disorders or arrhythmias. We prefer to classify them as wide complex tachyarrhythmia's or ventricular arrhythmias inclusive of malignant ventricular arrhythmias which with hemodynamic compromise is usually life threatening. Long term and fatality predictions warranting AICD implantation are already available. We have a novel method and robust algorithm with preprocessing and optimal feature selection from ECG signal analysis for such rhythm disorders. Variability of ECG recording makes predictability analysis challenging especially when execution time is of prime importance in tackling resuscitative attempts for MVA. Noisy data needs ltering and preprocessing for effective analysis. Portable devices need more of this ltering prior to data input. Deterministic probabilistic nite state automata (DPFA) which generates a probability strings from the broad morphologic patterns of an ECG can generate a classier data for the algorithm without preprocessing for atrial high rate episodes (AHRE). DPFA can be effectively used for atrial tachyarrhythmias for predictive analysis. The method we suggest is use of optimal classier set for prediction of malignant ventricular arrhythmias and use of DFPA for atrial arrhythmias. Here traditional practices of heart rate variability based support vector machine (SVM), discrete wavelet transform (DWT), principal component analysis (PCA), deep neural network (DNN), convoutional neural network (CNN) or CNN with long term memory (LSTM) can be outperformed. AICD - automatic implantable cardiac debrillator, MVA - Malignant Ventricular Arrhythmias, VT - ventricular tachycardia, VF - ventricular brillation,DFPA deterministic probabilistic nite state automata, SVM -Support Vector Machine, DWT discrete wavelet transform, PCA principal component analysis, DNN deep neural network, CNN convoutional neural network, Convoutional LSTM Long short term memory,RNN recurrent neural network

Neural Network-Based Approach for Face Recognition Across Varying Pose

2015 ◽  
Vol 29 (08) ◽  
pp. 1556015 ◽  
Author(s):  
Seyed Omid Shahdi ◽  
S. A. R. Abu-Bakar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Local Information ◽  
Discrete Wavelet ◽  
Face Images ◽  
The Face ◽  
The Relationship

At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

Radial Basis Neural Network with Gabor Wavelet Transform for Face Recognition

2013 ◽  
Vol 321-324 ◽  
pp. 1055-1060
Author(s):  
Guo Wei Yang ◽  
Wen Yi Cao ◽  
Feng Chang Xu
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Rbf Neural Network ◽  
Principal Component ◽  
Histogram Equalization ◽  
Gabor Wavelet ◽  
Radial Basis Neural Network ◽  
Gabor Wavelet Transform ◽  
Radial Basis ◽  
The Face

At first, in the paper, through the preprocessing of face image by the histogram equalization, enhance the image contrast. Then this paper uses Gabor wavelet transform, by choosing different size and direction to facial features extraction and Principal Component Analysis method for dimension reduction. Finally, input the face feature into the Radial Basis Function neural network for identification. Based on RBF neural network is trained and weights optimization, the efficiency of identification had been improved.

scholarly journals A New Convolutional Neural Network based on a Saprse Convolutional Layer for Animal Face Detection

2021 ◽  
Author(s):  
Islem Jarraya ◽  
Fatma BenSaid ◽  
Wael Ouarda ◽  
Umapada Pal ◽  
Adel Alimi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Feature Selection Method ◽  
Automated Negotiation ◽  
The Face ◽  
Online Feature Selection ◽  
Fully Connected ◽  
Sparse Feature Selection

This paper focuses on the face detection problem of three popular animal cat-egories that need control such as horses, cats and dogs. To be precise, a new Convolutional Neural Network for Animal Face Detection (CNNAFD) is actu-ally investigated using processed filters based on gradient features and applied with a new way. A new convolutional layer is proposed through a sparse feature selection method known as Automated Negotiation-based Online Feature Selection (ANOFS). CNNAFD ends by stacked fully connected layers which represent a strong classifier. The fusion of CNNAFD and MobileNetV2 constructs the newnetwork CNNAFD-MobileNetV2 which improves the classification results and gives better detection decisions. Our work also introduces a new Tunisian Horse Detection Database (THDD). The proposed detector with the new CNNAFD-MobileNetV2 network achieved an average precision equal to 99.78%, 99% and 98.28% for cats, dogs and horses respectively.

Sign in / Sign up

Export Citation Format

Share Document