2D and 3D palmprint information, PCA and HMM for an improved person recognition performance

Meraoumia, Abdallah; Chitroub, Salim; Bouridane, Ahmed

doi:10.3233/ica-130431

Cited by 36 publications

(17 citation statements)

References 24 publications

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“…Significant features are fed to a Gaussian mixture model classifier for the classification of sleep stages and a classification accuracy of 88.7% is obtained. Vural and Yildiz [95] used the principal component analysis [96] for the classification of hybrid features and reported an accuracy of 69.98%. Langkvist et al [97] performed sleep stage classification using deep belief nets, an unsupervised feature learning approach.…”

Section: Discussionmentioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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Background: The brain's continuous neural activity during sleep can be monitored by electroencephalogram (EEG) signals. The EEG wave pattern and frequency vary during five stages of sleep. These subtle variations in sleep EEG signals cannot be easily detected through visual inspection. Summary: A range of time, frequency, time-frequency and nonlinear analysis methods can be applied to understand the complex physiological signals and their chaotic behavior. This paper presents a comprehensive comparative review and analysis of 29 nonlinear dynamics measures for EEG-based sleep stage detection. Key Messages: The characteristic ranges of these features are reported for the five different sleep stages. All nonlinear measures produce clinically significant results, that is, they can discriminate the individual sleep stages. Feature ranking based on the statistical F-value, however, shows that the third order cumulant of higher order spectra yields the most discriminative result. The distinct value ranges for each sleep stage and the discriminative power of the features can be used for sleep disorder diagnosis, treatment monitoring, and drug efficacy assessment.

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Section: Discussionmentioning

confidence: 99%

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

et al. 2015

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“…Ambulation is a well studied topic [29] and there are enormous differences in movements depending on age: the older the individual, the smaller the degree of movement. Thus, the evaluation of frequency related patterns is not suitable as it varies with age and individuals.…”

Section: Design Decisions Related To Wearable Devices For Early Strokmentioning

confidence: 99%

“…FS filtering is a very interesting voting algorithm for the fusion of the results of different Principal Component Analyses (PCA, [14,29,51]). Whenever PCA is applied, the rollback of the M most relevant transformations -up to 95% of representation-is performed.…”

Section: Human Activity Recognition Using Gffsmmentioning

confidence: 99%

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A hybrid intelligent recognition system for the early detection of strokes

Villar

Chira

Sedano

et al. 2015

ICA

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Abstract. The increasing prevalence of wearable sensors and low-cost mobile devices have prompted the development of systems for automated diagnosis. Here we focus on models and algorithms for the early detection of strokes that are implanted in a wearable device that generates warning alarms and automatically connects to e-health services, ensuring timely interventions at the onset of a stroke. The proposed approach employs two wearable devices to monitor movement data that involve two main stages: Human Activity Recognition (HAR) and alarm generation. Two different HAR methods capable of classifying current human activity are developed and compared: one uses genetic fuzzy finite-state machines, and the other relies on Time Series (TS) analysis. Furthermore, an algorithm using Symbolic Aggregate approXimation (SAX) TS representation is proposed for alarm generation purposes, which is triggered by the detection of anomalous movements. The proposed methods are evaluated using realistic data gathered from healthy individuals. A discussion of topics related to the learning issues involved in these techniques is included. It is worth mentioning that the proposed algorithms can be easily transferred to embedded systems and can benefit from reduced learning costs.

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“…Numerous identification systems which use/ combine different types of biometrics have been proposed. The most important biometric information are DNA, iris, face, signature, fingerprint, voice, gait and palmprint [17]. When we compare their respective advantages and drawbacks, we must conclude that face recognition belongs to the most progressive biometric techniques today.…”

Section: Introductionmentioning

confidence: 99%

Local binary pattern based face recognition with automatically detected fiducial points

Lenc

Král

2016

ICA

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Abstract. This paper deals with automatic face recognition in the context of a real application for person identification developed for the Czech News Agency (ČTK) . We focus on popular Local Binary Patterns (LPBs) that are frequently used in this field with high recognition accuracy. One drawback of current LBP based methods is that the positions and number of the fiducial points are fixed. These points thus do not reflect the properties of a particular image whereas we believe it is beneficial to identify the most representative ones. The main contribution consists in proposing and comparing several LBP-based approaches that detect such points fully automatically. We use a set of Gabor filters for this task. Local extrema in the filter responses are detected and then used as the feature points. The number of points is further reduced by a clustering algorithm. Our approaches also differ from the other ones in the matching procedure. The proposed methods are evaluated on three standard corpora: ORL, FERET, AR face database and ourČTK dataset containing uncontrolled face images. Recognition results clearly show high quality of the proposed approaches that outperform significantly the baseline LBP approach on all corpora. The benefits of our methods are particularly evident in the case of real non-controlled images (ČTK corpus) where the accuracy is increased by more than 20% in absolute value.

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2D and 3D palmprint information, PCA and HMM for an improved person recognition performance

Cited by 36 publications

References 24 publications

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

Nonlinear Dynamics Measures for Automated EEG-Based Sleep Stage Detection

A hybrid intelligent recognition system for the early detection of strokes

Local binary pattern based face recognition with automatically detected fiducial points

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