Sherif N. Abbas

Biometric authentication based on PCG and ECG signals: present status and future directions

Due to the great advances in biomedical digital signal processing, new biometric traits have showed noticeable improvements in authentication systems. Recently, the ElectroCardioGram (ECG) and the PhonoCardioGraph (PCG) have been proposed as novel biometrics. This paper aims to review the previous studies related to the usage of the ECG and PCG signals in human recognition. In addition, we discuss briefly the most important techniques and methodologies used by researchers in the preprocessing, feature extraction and classification of the ECG and PCG signals. At the end, we introduce some future considerations that can be applied in this topic such as: the fusion between different techniques previously used, use both ECG and PCG signals in a multimodal biometric authentication system and building a prototype system for real-time authentication.

State-of-the-art methods and future perspectives for personal recognition based on electroencephalogram signals

In the past decade, biomedical instrumentations have witnessed major developments and now it is very easy to measure human biomedical electrical signals. One of these signals is the brain waves, known as electroencephalogram (EEG) signals, which became very easy to be measured using portable devices and dry electrodes. This opens the way for the use of brain waves in different applications rather than the biomedical diagnosis. One of the most recent non-medical applications for brain waves is the biometric authentication. Brain waves have some advantages which are not present in the commonly used identifiers, such as face and fingerprints, making them robust to spoof attacks. However, brain waves still face many challenges with reference to permanence and uniqueness. In this study, the authors discuss the employment of brain signals for human recognition tasks and focus on the challenges facing these signals towards the deployment of a practical biometric system. This study, also, provides a comprehensive review of the proposed approaches developed in EEG-based biometric authentication systems.

A Novel Biometric Approach for Human Identification and Verification Using Eye Blinking Signal

In this letter, a novel technique is adopted for human recognition based on eye blinking waveform extracted from electro-oculogram signals. For this purpose, a database of 25 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted and applied for identification and verification tasks. The pre-processing stage includes empirical mode decomposition to isolate electro-oculogram signal from brainwaves. Then, time delineation of the eye blinking waveform is utilized for feature extraction. Finally, linear discriminant analysis is adopted for classification. Based on the achieved results, the proposed system can identify subjects with best accuracy of 97.3% and verify them with an equal error rate of 3.7%. The obtained results in this letter confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.

A new multi-level approach to EEG based human authentication using eye blinking

A new multi-level EEG biometric authentication system based on eye blinking EOG signals is proposed.Eye blinking features based on time delineation.Two approaches for the proposed multi-level system based on feature and score level fusion.Multi-level system achieved higher recognition rates than single level one using EEG only. This letter proposes a new multi-level approach for human biometric authentication using Electro-Encephalo-Gram (EEG) signals (brain waves) and eye blinking Electro-Oculo-Gram (EOG) signals. The main objective of this letter is to improve the performance of the EEG based biometric authentication using eye blinking EOG signals which are considered as source of artifacts for EEG. Feature and score level fusion approaches are tested for the proposed multi-level system. Density based and canonical correlation analysis strategies are applied for the score and feature level fusions, respectively. Autoregressive modeling of EEG signals (during relaxation or visual stimulation) and time delineation of the eye blinking waveform are adopted for the feature extraction stage. Finally, the classification stage is performed using linear discriminxant analysis. For evaluation, a database of 31 subjects performing three different tasks of relaxation, visual stimulation, and eye blinking was collected using Neursky Mindwave headset. Using eye blinking features, a significant improvement is achieved, in terms of correct recognition and equal error rates, for the proposed multi-level EEG biometric system over single level system using EEG only.

PCG biometric identification system based on feature level fusion using canonical correlation analysis

In this paper, a new technique for human identification task based on heart sound signals has been proposed. It utilizes a feature level fusion technique based on canonical correlation analysis. For this purpose a robust pre-processing scheme based on the wavelet analysis of the heart sounds is introduced. Then, three feature vectors are extracted depending on the cepstral coefficients of different frequency scale representation of the heart sound namely; the mel, bark, and linear scales. Among the investigated feature extraction methods, experimental results show that the mel-scale is the best with 94.4% correct identification rate. Using a hybrid technique combining MFCC and DWT, a new feature vector is extracted improving the systems performance up to 95.12%. Finally, canonical correlation analysis is applied for feature fusion. This improves the performance of the proposed system up to 99.5%. The experimental results show significant improvements in the performance of the proposed system over methods adopting single feature extraction.

A new biometric modality for human authentication using eye blinking

This paper proposes a new biometric identifier for humans based on eye blinking waveform extracted from brain waves. Brain waves were recorded using Neurosky Mindwave headset from 25 volunteers. Two approaches are adopted for the pre-processing stage; the first approach uses empirical mode decomposition to isolate electro-oculogram signal from brain waves, then, extracts eye blinking signal. The second approach extracts eye blinking signal directly from brain waves. Features are extracted based on time delineation of the eye blinking waveform and classified using linear discriminant analysis. The best correct identification and equal error rates achieved are 98.51% and 2.5% for identification and verification modes respectively. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.

Eye Blinking EOG Signals as Biometrics

In this chapter, the feasibility of using eye blinking Electro-Oculo-Gram (EOG) signal as a new biometric trait for human identity recognition is tested. For this purpose, raw Electro-Encephalo-Gram (EEG) signals were recorded from 40 volunteers while performing the task of eye blinking. These signals were recorded using portable EEG headset, known as Neurosky Mindwave, which has wireless and dry electrodes at Fp1 position above the left eye. This makes it practical for biometric applications and for measuring EOG signals. For pre-processing, Discrete Wavelet Transform (DWT) is adopted to isolate EOG signals from brainwaves. Then, the onset and the offset of the eye blinking waveforms in the EOG signals are detected. After that, features are extracted using time delineation of the eye blinking waveform where important marks like the amplitude, position, and duration of the positive and negative pulses of the eye blinking waveform are employed as features. Finally, Discriminant Analysis (DA) classifier is used for classification. Moreover, a feature selection technique based on differential evolution is added for the proposed system. The best Correct Recognition Rate (CRR) achieved is 93.75 %. In verification mode, the lowest Equal Error Rate (EER) achieved is 7.45 %. Also, the permanence issue is evaluated using training and testing samples with different time separation between them. The optimistic results achieved in this chapter direct the scientific research to study different approaches for human identification using eye blinking to increase system’s performance. Moreover, eye blinking EOG biometric trait can be fused with other traits like EEG signals to build a multi-modal system to improve the performance of the EEG-based biometric authentication systems.

A new biometric authentication system using heart sounds based on wavelet packet features

In this paper, a new approach for human recognition using heart sounds is proposed. The new approach is based mainly on extracting features from heart sounds using wavelet packet decomposition. Different linear and non-linear filter banks at different decomposition levels are designed using wavelet packet decomposition to select the appropriate bases for extracting discriminant features. Automatic wavelet de-noising and linear discriminant analysis are adopted for pre-processing and classification stages, respectively. The proposed system is tested using an open database for heart sounds known as HSCT-11 which contains data collected from 206 subjects. Based on the achieved results, the proposed system can identify subjects with best accuracy of 91.05% and verify them with an equal error rate of 3.2%. The obtained results in this paper show that wavelet packet based features are appropriate for human recognition task using heart sounds.

Biometrics from heart sounds

A new PCG biometric authentication system using wavelet packet cepstral features is proposed.Non-linear wavelet packet filter bank is designed to match the acoustic nature of PCG signals.High correct recognition rates (91.05%) and low error rates (3.2%) were achieved for the proposed system.Higher recognition rates and lower error rates than previously implemented systems were achieved. Display Omitted This paper introduces a new approach for human recognition using heart sounds. The main contribution of this paper involves adopting wavelet packet cepstral coefficients as new features for heart sound signals in biometric applications. The proposed features utilize a non-linear wavelet packet filter banks which are designed to match the acoustic nature of the heart sound. The proposed system is evaluated using an open database for heart sounds known as HSCT-11 which contains data collected from 206 users. Based on the achieved results, the proposed system can identify users with best accuracy of 91.05% and verify them with an equal error rate of 3.2% using 200-fold random validation (random sub-sampling). The experimental results showed higher correct recognition rates and lower error rates in identification and verification modes, respectively, compared to previously implemented systems evaluated on the same database (HSCT-11).

Heart-ID: human identity recognition using heart sounds based on modifying mel-frequency cepstral features

This study presents a new framework for human identity recognition using heart sound signals. The proposed framework is based on extracting cepstral features from heart sound signals, which are known as phono-cardio-gram (PCG). Two well-known cepstral features have been adopted in most of the previously implemented PCG biometric authentication systems; namely, mel-frequency and linear frequency cepstral features. In this study, two more cepstral features are proposed based on modifying the mel-frequency cepstral features. The first one is based on modifying the mel-frequency equation to increase the non-linearity of the triangular filters in the frequency range of the PCG signal. The other is based on replacing mel-scaled triangular filters with wavelet packet filters where a non-linear filter bank structure is designed using wavelet packet decomposition to select the appropriate bases for extracting discriminant features. The proposed system uses wavelet de-noising for pre-processing and linear discriminant analysis for classification. The proposed system is evaluated on two databases; one consists of 21 users (BioSec. database) and the other consists of 206 users (HSCT-11 database). Moreover, the proposed system is compared with previous systems that used the same databases. On the basis of the achieved results over the two databases, the two proposed cepstral features achieved higher correct recognition rates and lower error rates in identification and verification modes, respectively.