No abstract

These days the wide usage of data has opened security vulnerabilities everywhere. This has led to research in the biometrics area for improving security. Presently with wide development of technology different forms of biometrics are being used in various applications. Thus, fingerprint and face are no longer the only ones being used in this field. The authors have concentrated on PCG as a biometric in this chapter. A very few sources are available in this area deeming it to be nascent. Recent proposals were examined, and it was observed that PCG reduces the risks of vulnerability faced by other biometric system. A simple biometric system would consist of steps like preprocessing, segmentation, feature extraction, and comparison or matching phase. In this chapter, some pre-processing steps as implemented by various authors using wavelets and other feature extraction techniques, implemented for the PCG biometric system by various researchers, are reviewed. Later, in the matching phase, Euclidean distance, GMM, FSR, VQ method are examined.

art-method. This article does not contain any studies with human participants performed by any of the authors. However, the data was collected by EPFL which is available open for the researchers to work upon.

No abstract

Brain-computer interfaces ( BCIs ) acquire electroencephalogram (EEG) signals and interpret them into a command that helps people with severe motor disabilities using single channel. The goal of BCI is to achieve a prototype that supports disabled people to develop the relevant function. Various studies have been implemented in the literature to achieve a superior design. The main novelty of the proposed P300 detection-based BCI model is associated with the usage of a single-channel. In this work, we introduced a denoising approach using the bandpass filter technique followed by the transformation of scalogram images using continuous wavelet transform. The derived images were trained and validated using a deep neural network based on the transfer learning approach. This paper presents a BCI model based on the deep network that delivers higher performance in terms of classification accuracy and bitrate for disabled subjects using a single-channel EEG signal. The proposed P300 based BCI model has the highest average information transfer rates of 13.23 to 26.48 bits/min for disabled subjects. The classification performance has shown that the deep network based on the transfer learning approach can offer comparable performance with other state-of-the-art-method. This article does not contain any studies with human participants performed by any of the authors. However, the data was collected by EPFL which is available open for the researchers to work upon.