Subtle Facial Expression Recognition in Still Images and Videos

2010 ◽  
pp. 259-278
Author(s):  
Fadi Dornaika ◽  
Fadi Dornaika ◽  
Bogdan Raducanu ◽  
Bogdan Raducanu
Keyword(s):  
Discriminant Analysis ◽  
Facial Expressions ◽  
Support Vector ◽  
Expression Recognition ◽  
Learning Approaches ◽  
Still Images ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Dynamic Recognition ◽  
Efficient Recognition

This chapter addresses the recognition of basic facial expressions. It has three main contributions. First, the authors introduce a view- and texture independent schemes that exploits facial action parameters estimated by an appearance-based 3D face tracker. they represent the learned facial actions associated with different facial expressions by time series. Two dynamic recognition schemes are proposed: (1) the first is based on conditional predictive models and on an analysis-synthesis scheme, and (2) the second is based on examples allowing straightforward use of machine learning approaches. Second, the authors propose an efficient recognition scheme based on the detection of keyframes in videos. Third, the authors compare the dynamic scheme with a static one based on analyzing individual snapshots and show that in general the former performs better than the latter. The authors then provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM).

scholarly journals Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data

2019 ◽  
Vol 11 (10) ◽  
pp. 1195 ◽  
Author(s):  
Minsang Kim ◽  
Myung-Sook Park ◽  
Jungho Im ◽  
Seonyoung Park ◽  
Myong-In Lee
Keyword(s):  
Machine Learning ◽  
Tropical Cyclone ◽  
Surface Wind ◽  
Support Vector ◽  
Learning Approaches ◽  
Yearly Variation ◽  
Linear Discriminant ◽  
Forecast Lead Time ◽  
Vector Machines ◽  
First Time

This study compared detection skill for tropical cyclone (TC) formation using models based on three different machine learning (ML) algorithms-decision trees (DT), random forest (RF), and support vector machines (SVM)-and a model based on Linear Discriminant Analysis (LDA). Eight predictors were derived from WindSat satellite measurements of ocean surface wind and precipitation over the western North Pacific for 2005–2009. All of the ML approaches performed better with significantly higher hit rates ranging from 94 to 96% compared with LDA performance (~77%), although false alarm rate by MLs is slightly higher (21–28%) than that by LDA (~13%). Besides, MLs could detect TC formation at the time as early as 26–30 h before the first time diagnosed as tropical depression by the JTWC best track, which was also 5 to 9 h earlier than that by LDA. The skill differences across MLs were relatively smaller than difference between MLs and LDA. Large yearly variation in forecast lead time was common in all models due to the limitation in sampling from orbiting satellite. This study highlights that ML approaches provide an improved skill for detecting TC formation compared with conventional linear approaches.

scholarly journals Discriminating Quasars from Stars Based on SDSS and UKIDSS Databases

2012 ◽  
Vol 8 (S295) ◽  
pp. 180-180
Author(s):  
He Ma ◽  
Yanxia Zhang ◽  
Yongheng Zhao ◽  
Bo Zhang
Keyword(s):  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Experimental Result ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Joint Approach

AbstractIn this work, two different algorithms: Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs) are combined for the classification of unresolved sources from SDSS DR8 and UKIDSS DR8. The experimental result shows that this joint approach is effective for our case.

scholarly journals ECG Based Biometric uing Wavelet Packet Decomposition

2019 ◽  
Vol 9 (1) ◽  
pp. 2178-2183 ◽  
Keyword(s):  
Discriminant Analysis ◽  
Wavelet Packet ◽  
Support Vector ◽  
Frequency Noise ◽  
Wavelet Packet Decomposition ◽  
Linear Discriminant ◽  
Biometric Technology ◽  
Vector Machines ◽  
And Performance ◽  
Electrocardiogram Ecg

Biometric technology has been commonly used for authentication. Fingerprint or iris become one of the biometrics that is widely applied. However, this type of biometrics tends to be easily falsified and damaged. So it is misused for manipulating actions and even crime. Therefore a new biometric method is needed to overcome this problem. One potential modality is biometrics based on an electrocardiogram (ECG) signal. This research simulates a one-lead ECG waveform for person authentication. ECG waves were taken from eleven healthy adult volunteers with a length of 60 seconds. ECG waves from each person are segmented into 10 sections so that a total of 110 ECG waves are used for person authentication simulations. All noise of the ECG waves was removed using a bandpass filter to reduce artifacts and high-frequency noise. Wavelet packet decomposition (3 Level) was applied to decompose the signal in several intrinsic parts so that typical wave information can be retrieved. Entropy-based feature extraction applied to all decomposed signals. A total of 14 entropy features have been calculated and used as predictors in the classification process. Validation and performance tests are carried out by cross-validation combined with linear discriminant analysis and support vector machines with five scenarios. The proposed method provides the highest accuracy of 71.8% using discriminant analysis and cubic support vector machine. The best accuracy value was achieved if all entropy features from all wavelet decomposition levels are used as predictors in the classification process. This research is expected to be a reference that ECG has the potential to become a future biometric modality

scholarly journals Using Linear Discriminant Analysis for Dimensionality Reduction for Predicting Anomalies of BGP data

2019 ◽  
Vol 8 (11) ◽  
pp. 1989-1995
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
High Dimensions ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Data Packets ◽  
Vector Machines

Border Gateway Protocol (BGP) is a vital protocol on the internet for transfer of data packets among Autonomous System (AS). Security is a major concern for the transmission of BGP packets which are often attacked by worms or are hijacked by an attacker which results in requests entering black holes or loss of connection to the particular sites. The BGP anomalies can be reduced by analyzing the BGP datasets. Since, ASes communicate through messages, therefore, the anomalies can be reduced by identifying the corrupted BGP message in the dataset. In this paper, BGP anomalies have been classified by applying Machine learning (ML) algorithms. The dataset contains information about the sending and receiving time between ASes. The classifiers were used to predict the anomalies. Since the dataset had high dimensions, the dimensions were reduced using Linear Discriminant Analysis (LDA) and then Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Linear Regression, Logistic Regression and Multi-Layer Perceptron (MLP) have been used to classify the anomalies.

scholarly journals Linear discriminant analysis and support vector machines for classifying breast cancer

2021 ◽  
Vol 10 (1) ◽  
pp. 253
Author(s):  
Zuherman Rustam ◽  
Yasirly Amalia ◽  
Sri Hartini ◽  
Glori Stephani Saragih
Keyword(s):  
Breast Cancer ◽  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Linear Discriminant ◽  
Specialist Doctor ◽  
Vector Machines

<span id="docs-internal-guid-4db59d91-7fff-c659-478a-6dd7456f380f"><span>Breast cancer is an abnormal cell growth in the breast that keeps changed uncontrolled and it forms a tumor. The tumor can be benign or malignant. Benign could not be dangerous to health and cancerous, but malignant could be has a probability dangerous to health and be cancerous. A specialist doctor will diagnose the patient and give treatment based on the diagnosis which is benign or malignant. Machine learning offer times efficiency to determine a cancer cell. The machine will learn the pattern based on the information from the dataset. Support vector machines and linear discriminant analysis are common methods that can be used in the classification of cancer. In this study, both of linear discriminant analysis and support vector machines are compared by looking from accuracy, sensitivity, specificity, and F1-score. We will know which methods are better in classifying breast cancer dataset. The result shows that the support vector machine has better performance than the linear discriminant analysis. It can be seen from the accuracy is 98.77%.</span></span>

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