Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability
Force field feature extraction for ear biometrics
Computer Vision and Image Understanding
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
People identification with RMS-Based spatial pattern of EEG signal
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Multimodal biometric system combining ECG and sound signals
Pattern Recognition Letters
| Hi-index | 0.00 |
The energy of brain potentials evoked during processing of visual stimuli is considered as a new biometric. In particular, we propose several advances in the feature extraction and classification stages. This is achieved by performing spatial data/sensor fusion, whereby the component relevance is investigated by selecting maximum informative (EEG) electrodes (channels) selected by Davies---Bouldin index. For convenience and ease of cognitive processing, in the experiments, simple black and white drawings of common objects are used as visual stimuli. In the classification stage, the Elman neural network is employed to classify the generated EEG energy features. Simulations are conducted by using the hold-out classification strategy on an ensemble of 1,600 raw EEG signals, and 35 maximum informative channels achieved the maximum recognition rate of 98.56驴卤驴1.87%. Overall, this study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud.