Data Fusion Applied to Biometric Identification – A Review

Zapata, J. C.; Duque-Mejía, C.; Rojas-Idarraga, Y.; González, Marina; Guzmán, Jaime A.; Becerra, Miguel A.

doi:10.1007/978-3-319-66562-7_51

Cited by 18 publications

(9 citation statements)

References 67 publications

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“…He and Tan [ 42 ] developed a new entropy-based PCA approach for dimensionality reduction. Zapata et al [ 43 ] summarized the state-of-the-art data fusion oriented to biometric authentication and identification, exploring its techniques, benefits, advantages, disadvantages, and challenges. An interesting work is to observe the amplitude values of the Q, R, and S waves to identify an individual, not the intervals between fiducial points [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Lee

Kim

2018

Sensors

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The electrocardiogram (ECG) waveform conveys information regarding the electrical property of the heart. The patterns vary depending on the individual heart characteristics. ECG features can be potentially used for biometric recognition. This study presents a new method using the entire ECG waveform pattern for matching and demonstrates that the approach can potentially be employed for individual biometric identification. Multi-cycle ECG signals were assessed using an ECG measuring circuit, and three electrodes can be patched on the wrists or fingers for considering various measurements. For biometric identification, our-fold cross validation was used in the experiments for assessing how the results of a statistical analysis will generalize to an independent data set. Four different pattern matching algorithms, i.e., cosine similarity, cross correlation, city block distance, and Euclidean distances, were tested to compare the individual identification performances with a single channel of ECG signal (3-wire ECG). To evaluate the pattern matching for biometric identification, the ECG recordings for each subject were partitioned into training and test set. The suggested method obtained a maximum performance of 89.9% accuracy with two heartbeats of ECG signals measured on the wrist and 93.3% accuracy with three heartbeats for 55 subjects. The performance rate with ECG signals measured on the fingers improved up to 99.3% with two heartbeats and 100% with three heartbeats of signals for 20 subjects.

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Section: Introductionmentioning

confidence: 99%

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Lee

Kim

2018

Sensors

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“…Current advances in bio-signal sensors, data acquisition, embedded systems and processing techniques contribute to the integration of physiological signals in a wide variety of clinical and non-clinical settings like medical diagnosis [1], biorobotics [2], brain-computer interfaces (BCI) or brainmachine interfaces (BMI) [3], biometrics [4]. These biosignals use different modalities: the electrocardiography (ECG), the electromyography (EMG), the electrooculography (EOG), the electrocorticography (ECoG), the electroencephalography (EEG), the positron emission tomography (PET), the magnetic resonance imaging (MRI), the functional MRI (fMRI), and the diffusion tensor imaging (DTI).…”

Section: Introductionmentioning

confidence: 99%

WPT-ANN and Belief Theory Based EEG/EMG Data Fusion for Movement Identification

Sbargoud¹,

Djeha²,

Guiatni³

et al. 2019

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The electromyography (EMG) and electroencephalography (EEG) are two frequently used modalities of bio-signals in the field of bio-robotics. However, it is insufficient to use a single isolated modality, due to the presence of artifacts, the lack of information, etc. To solve the problem, this paper proposes an EEG/EMG data fusion method that take advantages of both signals and overcome their drawbacks to achieve accurate identification of movements. The two types of bio-signals were preprocessed through wavelet packets transform (WPT) and classified by the artificial neural network (ANN). Then, the belief theory was introduced to allow for model uncertainty and imprecision, which adapts to the ambiguities and conflicts between sources. Experimental results show that the proposed EEG/EMG data fusion method outperformed the strategies based on only one modality. The research findings provide new impetus to bio-robotic applications.

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“…There are several sensor fusion techniques, applied to the sensors embedded in smartphones, smartwaches, as a means to help identify the mobile device user’s daily activities or sport exercises. Sensor data fusion methods help to consolidate the signals collected from different body sensors, increasing the performance of the algorithms for the recognition of the different activities [ 28 , 29 , 30 , 31 , 32 ]. However, due to low memory, low battery life and low processing power constraints, some data fusion techniques are not suited to this scenario.…”

Section: Introductionmentioning

confidence: 99%

Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization

Biagetti

Crippa

Falaschetti

et al. 2018

Sensors

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The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare of elderly people. The activity diarization is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a technique for data fusion at classifier level of accelerometer and sEMG signals acquired by using a low-cost wearable wireless system for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications. To demonstrate the capability of the system of diarizing the user’s activities, data recorded from a few subjects were used to train and test the automatic classifier for recognizing the type of exercise being performed.

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Data Fusion Applied to Biometric Identification – A Review

Cited by 18 publications

References 67 publications

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

Individual Biometric Identification Using Multi-Cycle Electrocardiographic Waveform Patterns

WPT-ANN and Belief Theory Based EEG/EMG Data Fusion for Movement Identification

Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization

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