Influence of the Intra-Modal Facial Information for an Identification Approach

2014 ◽  
pp. 318-342
Author(s):  
Carlos M. Travieso ◽  
Marcos del Pozo-Baños ◽  
Jaime R. Ticay-Rivas ◽  
Jesús B. Alonso
Keyword(s):  
Principal Component ◽  
Component Analysis ◽  
Multimodal Fusion ◽  
Support Vector ◽  
Identification System ◽  
Facial Features ◽  
Classifier Systems ◽  
Lighting Conditions ◽  
Facial Information ◽  
Identification Approach

This chapter presents a comprehensive study on the influence of the intra-modal facial information for an identification approach. It was developed and implemented a biometric identification system by merging different intra-multimodal facial features: mouth, eyes, and nose. The Principal Component Analysis, Independent Component Analysis, and Discrete Cosine Transform were used as feature extractors. Support Vector Machines were implemented as classifier systems. The recognition rates obtained by multimodal fusion of three facial features has reached values above 97% in each of the databases used, confirming that the system is adaptive to images from different sources, sizes, lighting conditions, etc. Even though a good response has been shown when the three facial traits were merged, an acceptable performance has been shown when merging only two facial features. Therefore, the system is robust against problems in one isolate sensor or occlusion in any biometric trait. In this case, the success rate achieved was over 92%.

scholarly journals Support Vector Machine for Behavior-Based Driver Identification System

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Huihuan Qian ◽  
Yongsheng Ou ◽  
Xinyu Wu ◽  
Xiaoning Meng ◽  
Yangsheng Xu
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Identification System ◽  
Dynamic Features ◽  
Experimental Platform ◽  
Driving Behaviors ◽  
The Individual ◽  
Driver Identification

We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.

scholarly journals The Influence of Each Facial Feature on How We Perceive and Interpret Human Faces

i-Perception ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 204166952096112
Author(s):  
Jose A. Diego-Mas ◽  
Felix Fuentes-Hurtado ◽  
Valery Naranjo ◽  
Mariano Alcañiz
Keyword(s):  
Principal Component Analysis ◽  
Personality Traits ◽  
Principal Component ◽  
Facial Feature ◽  
Component Analysis ◽  
Facial Features ◽  
Direct Link ◽  
Facial Information ◽  
Feature Based ◽  
Human Faces

Facial information is processed by our brain in such a way that we immediately make judgments about, for example, attractiveness or masculinity or interpret personality traits or moods of other people. The appearance of each facial feature has an effect on our perception of facial traits. This research addresses the problem of measuring the size of these effects for five facial features (eyes, eyebrows, nose, mouth, and jaw). Our proposal is a mixed feature-based and image-based approach that allows judgments to be made on complete real faces in the categorization tasks, more than on synthetic, noisy, or partial faces that can influence the assessment. Each facial feature of the faces is automatically classified considering their global appearance using principal component analysis. Using this procedure, we establish a reduced set of relevant specific attributes (each one describing a complete facial feature) to characterize faces. In this way, a more direct link can be established between perceived facial traits and what people intuitively consider an eye, an eyebrow, a nose, a mouth, or a jaw. A set of 92 male faces were classified using this procedure, and the results were related to their scores in 15 perceived facial traits. We show that the relevant features greatly depend on what we are trying to judge. Globally, the eyes have the greatest effect. However, other facial features are more relevant for some judgments like the mouth for happiness and femininity or the nose for dominance.

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

2021 ◽  
pp. 014233122110447
Author(s):  
Hongjuan Yao ◽  
Xiaoqiang Zhao ◽  
Wei Li ◽  
Yongyong Hui
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Objective Function ◽  
Principal Component ◽  
Batch Process ◽  
Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Order Information ◽  
Multiple Neighborhoods

Batch process generally has varying dynamic characteristic that causes low fault detection rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This paper proposes a global enhanced multiple neighborhoods preserving embedding based fault detection strategy for dynamic batch process. Firstly, the angle neighbor is defined and selected to compensate for the insufficient expression for the spatial similarity of samples only by using the distance neighbor, and the time neighbor is introduced to describe the time correlations between samples. These three types of neighbors can fully characterize the similarity of the samples in time and space. Secondly, considering the minimum reconstruction error and the order information of three types of neighbors, an enhanced objective function is constructed to prevent the loss of order information when neighborhood preserving embedding (NPE) calculates the reconstruction weights. Furthermore, the enhanced objective function and a global objective function are organically combined to extract both global and local features, to describe process dynamics and visualize process data in a low-dimensional space. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The advantages of the proposed method over principal component analysis, neighborhood preserving embedding, dynamic principal component analysis and time NPE are demonstrated by a numerical example and the penicillin fermentation process simulation.

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