Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Templates for the solution of algebraic eigenvalue problems: a practical guide
Templates for the solution of algebraic eigenvalue problems: a practical guide
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
AUSWIRELESS '07 Proceedings of the The 2nd International Conference on Wireless Broadband and Ultra Wideband Communications
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Using EEG spectral components to assess algorithms for detecting fatigue
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Driver drowsiness detection with eyelid related parameters by Support Vector Machine
Expert Systems with Applications: An International Journal
Driver Inattention Detection based on Eye Gaze-Road Event Correlation
International Journal of Robotics Research
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Face recognition using a fuzzy fisherface classifier
Pattern Recognition
A maximizing-discriminability-based self-organizing fuzzy network for classification problems
IEEE Transactions on Fuzzy Systems
Comparing combinations of EEG activity in train drivers during monotonous driving
Expert Systems with Applications: An International Journal
Real-time system for monitoring driver vigilance
IEEE Transactions on Intelligent Transportation Systems
Real-Time Detection of Driver Cognitive Distraction Using Support Vector Machines
IEEE Transactions on Intelligent Transportation Systems
Fuzzy logic approaches to structure preserving dimensionality reduction
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Application of a fuzzy discrimination analysis for diagnosis of valvular heart disease
IEEE Transactions on Fuzzy Systems
Uncertainty Estimation Using Fuzzy Measures for Multiclass Classification
IEEE Transactions on Neural Networks
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Driver drowsiness is reported as one of the main causal factors in many traffic accidents as it progressively impairs the driver's awareness about external events. Drowsiness detection can be approached through monitoring physiological signals while driving to correlate drowsiness with the change in the corresponding patterns of the Electroencephalogram (EEG), Electrooculogram (EOG), and Electrocardiogram (ECG) signals. The main challenge in such an approach is to extract a set of features that can highly discriminate between the different drowsiness levels. This paper proposes a new Fuzzy Neighborhood Preserving Analysis (FNPA) feature projection method that is used to extract the discriminant information relevant to the loss of attention caused by drowsiness. Unlike existing methods, FNPA considers the fuzzy memberships of the input measurements into the different classes while constructing the graph Laplacian. Thus, it is able to identify both the discriminant and the geometrical structure of the input data while accounting for the overlapping nature of the drowsiness patterns. Furthermore, in order to address the singularity problem that occurs in many real world problems, the singular value decomposition (SVD), and later the QR-Decomposition, are utilized to extract a set of statistically uncorrelated features presenting the Uncorrelated FNPA (UFNPA). In the current preliminary study with datasets collected from 31 subjects only, while performing a driving simulation task, the proposed method is capable of accurately classifying the drowsiness levels using a small number of features with an average accuracy of ~93%. On the other hand, the possibility of developing a subject-independent drowsiness recognition system is also investigated when the problem is converted into a binary classification task, as imposed by the number of drowsiness levels exhibited by the drivers, with accuracies ranging from 82%-to-84%.