AN EVALUATION ON PERFORMANCE OF PCA IN FACE RECOGNITION WITH EXPRESSION VARIATIONS

2018 ◽  
Vol 1 (30) ◽  
pp. 61-66
Author(s):  
Khanh Ngoc Van Duong ◽  
An Bao Nguyen
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Mouth Opening ◽  
Experimental Results ◽  
Classification Algorithms ◽  
Test Cases ◽  
Wide Mouth ◽  
Better Than

Appearance-based recognition methods often encounter difficulties when the input images contain facial expression variations such as laughing, crying or wide mouth opening. In these cases, holistic methods give better performance than appearance-based methods. This paper presents some evaluation on face recognition under variation of facial expression  using the combination of PCA and classification algorithms. The experimental results showed that the best accuracy can be obtained with very few eigenvectors and KNN algorithm (with k=1) performs better than SVM in most test cases.

scholarly journals A Comprehensive Evaluation of Approaches for Built-Up Area Extraction from Landsat OLI Images Using Massive Samples

2018 ◽  
Vol 11 (1) ◽  
pp. 2 ◽  
Author(s):  
Tao Zhang ◽  
Hong Tang
Keyword(s):  
Learning Strategies ◽  
Classification Accuracy ◽  
Feature Learning ◽  
Automatic Generation ◽  
Experimental Results ◽  
Support Vector ◽  
Feature Engineering ◽  
Classification Algorithms ◽  
Sample Points ◽  
Better Than

Detailed information about built-up areas is valuable for mapping complex urban environments. Although a large number of classification algorithms for such areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore, we launched a unique investigation to provide a full test of the Operational Land Imager (OLI) imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we proposed a method based on the European Space Agency’s (ESA) 38-m global built-up area data of 2014, OpenStreetMap, and MOD13Q1-NDVI to achieve the rapid and automatic generation of a large number of sample points. Our aim was to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape, and texture as the input features, and support vector machine (SVM), random forest (RF), and AdaBoost as the classification algorithms. In feature learning, the convolutional neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. The experimental results show the following: (1) The approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers (e.g., RF) are comparable to that of CNN. Two-dimensional CNN and the 7-neighborhood RF have the highest classification accuracies at nearly 91%; (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (e.g., PanTex (texture-derived built-up presence index) and enhanced morphological building index (EMBI)) can help improve classification accuracy. The code and experimental results are available at https://github.com/zhangtao151820/CompareMethod.

Localization and integration of diffraction spots from two-dimensional X-ray detector images by digital filtering

1997 ◽  
Vol 212 (7) ◽  
Author(s):  
M. Wahl ◽  
F. C. F. Körber
Keyword(s):  
Digital Filtering ◽  
Classification Algorithms ◽  
Test Cases ◽  
Two Dimensional ◽  
Unit Cell Parameters ◽  
X Ray ◽  
Cell Parameters ◽  
Spot Detection ◽  
Integrated Intensities ◽  
Better Than

AbstractA method has been developed to measure background corrected integrated intensities and their standard deviations from two-dimensional diffraction data without prior knowledge of the unit cell parameters or the crystal orientation. Ab initio spot detection and measurement are achieved by digital filtering and classification algorithms. Two test cases are presented showing that the method produces results which are comparable or better than those from standard processing programs.

scholarly journals Elastic Bunch Graph Matching Based Face Recognition Under Varying Lighting, Pose, and Expression Conditions

2016 ◽  
Vol 3 (4) ◽  
pp. 177 ◽  
Author(s):  
Farooq Ahmad Bhat ◽  
M. Arif Wani
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Graph Matching ◽  
Experimental Results ◽  
Lighting Condition ◽  
Expression Variation ◽  
Labelled Graphs

In this paper performance of elastic bunch graph matching (EBGM) for face recognition under variation in facial expression, variation in lighting condition and variation in poses are given. In this approach faces are represented by labelled graphs. Experimental results of EBGM on ORL, Yale B and FERET datasets are provided. Strong and weak features of EBGM algorithm are discussed.

scholarly journals Face Recognition Using Complex Valued Backpropagation

2018 ◽  
Vol 11 (2) ◽  
pp. 103
Author(s):  
Zumrotun Nafisah ◽  
Febrian Rachmadi ◽  
Elly Matul Imah
Keyword(s):  
Face Recognition ◽  
Research Area ◽  
Experimental Results ◽  
Activation Functions ◽  
Backpropagation Algorithm ◽  
Complex Valued ◽  
Better Than

Face recognition is one of biometrical research area that is still interesting. This study discusses the Complex-Valued Backpropagation algorithm for face recognition. Complex-Valued Backpropagation is an algorithm modified from Real-Valued Backpropagation algorithm where the weights and activation functions used are complex. The dataset used in this study consist of 250 images that is classified in 5 classes. The performance of face recognition using Complex-Valued Backpropagation is also compared with Real-Valued Backpropagation algorithm. Experimental results have shown that Complex-Valued Backpropagation performance is better than Real-Valued Backpropagation.

scholarly journals Celebrity Face Recognition using Deep Learning

2018 ◽  
Vol 12 (2) ◽  
pp. 476 ◽  
Author(s):  
Nur Ateqah Binti Mat Kasim ◽  
Nur Hidayah Binti Abd Rahman ◽  
Zaidah Ibrahim ◽  
Nur Nabilah Abu Mangshor
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Experimental Results ◽  
Learning Technique ◽  
Excellent Accuracy ◽  
Better Than

Face recognition is one of the well studied problems by researchers in computer visions.  Among the challenges of this task are the occurrence of different facial expressions like happy or sad, and different views of the images such as front and side views.  This paper experiments a publicly available dataset that consists of 200,000 images of celebrity faces. Deep Learning technique is gaining its popularity in computer vision and this paper applies this technique for face recognition problem.  One of the techniques under deep learning is Convolutional Neural Network (CNN).  There is also pre-trained CNN models that are AlexNet and GoogLeNet, which produce excellent accuracy results.  The experimental results indicate that AlexNet is better than basic CNN and GoogLeNet for face recognition.

Contribution of Color to Face Recognition

Perception ◽  
10.1068/p3376 ◽  
2002 ◽  
Vol 31 (8) ◽  
pp. 995-1003 ◽  
Author(s):  
Andrew W Yip ◽  
Pawan Sinha
Keyword(s):  
Image Analysis ◽  
Face Recognition ◽  
Recognition Performance ◽  
Past Research ◽  
Color Images ◽  
Experimental Results ◽  
Facial Attributes ◽  
Color Cues ◽  
Better Than

One of the key challenges in face perception lies in determining how different facial attributes contribute to judgments of identity. In this study, we focus on the role of color cues. Although color appears to be a salient attribute of faces, past research has suggested that it confers little recognition advantage for identifying people. Here we report experimental results suggesting that color cues do play a role in face recognition and their contribution becomes evident when shape cues are degraded. Under such conditions, recognition performance with color images is significantly better than that with gray-scale images. Our experimental results also indicate that the contribution of color may lie not so much in providing diagnostic cues to identity as in aiding low-level image-analysis processes such as segmentation.

scholarly journals Emotion Monitoring for Preschool Children Based on Face Recognition and Emotion Recognition Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guiping Yu
Keyword(s):  
Face Recognition ◽  
Preschool Children ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Experimental Results ◽  
Geometric Feature ◽  
Feature Descriptor ◽  
Expression Recognition ◽  
Facial Contour ◽  
Recognition Algorithms

In this paper, we study the face recognition and emotion recognition algorithms to monitor the emotions of preschool children. For previous emotion recognition focusing on faces, we propose to obtain more comprehensive information from faces, gestures, and contexts. Using the deep learning approach, we design a more lightweight network structure to reduce the number of parameters and save computational resources. There are not only innovations in applications, but also algorithmic enhancements. And face annotation is performed on the dataset, while a hierarchical sampling method is designed to alleviate the data imbalance phenomenon that exists in the dataset. A new feature descriptor, called “oriented gradient histogram from three orthogonal planes,” is proposed to characterize facial appearance variations. A new efficient geometric feature is also proposed to capture facial contour variations, and the role of audio methods in emotion recognition is explored. Multifeature fusion can be used to optimally combine different features. The experimental results show that the method is very effective compared to other recent methods in dealing with facial expression recognition problems about videos in both laboratory-controlled environments and outdoor environments. The method performed experiments on expression detection in a facial expression database. The experimental results are compared with data from previous studies and demonstrate the effectiveness of the proposed new method.

scholarly journals Research on Face Recognition Classification Based on Improved GoogleNet

2022 ◽  
Vol 2022 ◽  
pp. 1-6
Author(s):  
Zhigang Yu ◽  
Yunyun Dong ◽  
Jihong Cheng ◽  
Miaomiao Sun ◽  
Feng Su
Keyword(s):  
Face Recognition ◽  
Network Performance ◽  
Recall Rate ◽  
Experimental Results ◽  
Learning Technology ◽  
Learning Method ◽  
Learning Methods ◽  
Improve Accuracy ◽  
And Migration ◽  
Better Than

Face recognition is a relatively mature technology, which has some applications in many aspects, and now there are many networks studying it, which has indeed brought a lot of convenience to mankind in all aspects. This paper proposes a new face recognition technology. First, a new GoogLeNet-M network is proposed, which improves network performance on the basis of streamlining the network. Secondly, regularization and migration learning methods are added to improve accuracy. The experimental results show that the GoogLeNet-M network with regularization using migration learning technology has the best performance, with a recall rate of 0.97 and an accuracy of 0.98. Finally, it is concluded that the performance of the GoogLeNet-M network is better than other networks on the dataset, and the migration learning method and regularization help to improve the network performance.

THE SOURCES OF AUXIN HORMONE TO GROWTH OF SHOOT PINEAPPLE STEM CUTTING (Ananascomosus.L. Merr.)

2016 ◽  
Vol 1 (1) ◽  
pp. 45-52
Author(s):  
Palupi Puspitorini
Keyword(s):  
Water Concentration ◽  
Urine Concentration ◽  
Experimental Results ◽  
Stem Cuttings ◽  
Randomized Design ◽  
Natural Hormone ◽  
Cow Urine ◽  
Urine Concentrations ◽  
Completely Randomized Design ◽  
Better Than

The aim of this study was to select the best sources of auxin of which it can stimulate the growth of shoots Pineapple plant cuttings. This research is compiled in a completely randomized design (CRD) with 4 treatments and 6 replications. The Data were statistically Analyzed by the DMRT. Level of treatment given proves that no treatment 0%, cow urine concentration of 25%, young coconut water concentration of 25% and Rootone F 100 mg / cuttings. The results showed that cow urine concentrations of 25% and Rootone F 100 mg give the best results in stimulating the growth of shoots pineapple stem cuttings. Experimental results concluded that the effect of this natural hormone were better than the shoots without given hormone.           

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

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