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1

Personal identification based on brain networks of EEG signals

Kong W., Jiang B., Fan Q., Zhu L., Wei X.

International Journal of Applied Mathematics and Computer Science

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2018

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Vol. 28, no. 4

745--757

EN

Personal identification is particularly important in information security. There are numerous advantages of using electroencephalogram (EEG) signals for personal identification, such as uniqueness and anti-deceptiveness. Currently, many researchers focus on single-dataset personal identification, instead of the cross-dataset. In this paper, we propose a method for cross-dataset personal identification based on a brain network of EEG signals. First, brain functional networks are constructed from the phase synchronization values between EEG channels. Then, some attributes of the brain networks including the degree of a node, the clustering coefficient and global efficiency are computed to form a new feature vector. Lastly, we utilize linear discriminant analysis (LDA) to classify the extracted features for personal identification. The performance of the method is quantitatively evaluated on four datasets involving different cognitive tasks: (i) a four-class motor imagery task dataset in BCI Competition IV (2008), (ii) a two-class motor imagery dataset in the BNCI Horizon 2020 project, (iii) a neuromarketing dataset recorded by our laboratory, (iv) a fatigue driving dataset recorded by our laboratory. Empirical results of this paper show that the average identification accuracy of each data set was higher than 0.95 and the best one achieved was 0.99, indicating a promising application in personal identification.

2

A novel phase-intensive local pattern for periocular recognition under visible spectrum

Bakshi S., Sa P. K., Majhi B.

Biocybernetics and Biomedical Engineering

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2015

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Vol. 35, no. 1

30--44

EN

The article proposes a novel multi-scale local feature based on the periocular recognition technique which is capable of extracting high-dimensional subtle features existent in the iris region as well as low-dimensional gross features in the periphery skin region of the iris. A set of filter banks of different scales is employed to exploit the phase-intensive patterns in visible spectrum periocular image of a subject captured from a distance in partial non-cooperative scenario. The proposed technique is verified with experiments on near-infrared illumination databases like BATH and CASIA-IrisV3-Lamp. Experiments have been further extended to images from visible spectrum ocular databases like UBIRISv2 and low-resolution eye regions extracted from FERETv4 face database to establish that the proposed feature performs comparably better than existing local features. To find the robustness of the proposed approach, the low resolution visible spectrum images of mentioned databases are converted to grayscale images. The proposed approach yields unique patterns from these grayscale images. The ability to find coarse-to-fine features in multi-scale and different phases is accountable for the improved robustness of the proposed approach.

3

Wysoko wiarygodne metody identyfikacji osób

Olchowik W.

Biuletyn Wojskowej Akademii Technicznej

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2013

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Vol. 62, nr 4

179-193

PL

Spośród znanych technik biometrycznych identyfikacji osób zdecydowanie najwyższy poziom wiarygodności uzyskuje się poprzez porównanie profili DNA. W artykule opisano metodykę identyfikacji opartej o profile STR16. Opracowano oryginalne algorytmy identyfikacji osób i oceny pokrewieństwa bazujące na dwóch uniwersalnych wskaźnikach oraz dokonano statystycznej analizy wiarygodności poszczególnych hipotez. Przetwarzanie danych zostało zintegrowane z innymi biometrycznymi technikami identyfikacji.

EN

Amongst the known biometric authentication techniques, the highest rate of credibility is definitely attained by usage of DNA profiling. This paper describes the methodology of identification based on STR profiles. The created algorithms and methods will allow the identification and evaluation of kinship on the basis of two fundamental indicators. It is a different approach from the ones, which are currently being used. However, its credibility has been proven statistically. Data processing has been integrated with other biometric identification techniques.

4

Iris Features Extraction Using Beamlets and Wedgelets

Rachubiński M.

Machine Graphics and Vision

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2009

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Vol. 18, No. 3

289-304

EN

A new approach to iris feature extraction using geometrical wavelets is presented. Iris code is generated by using representation of the wavelet coefficients based on a wedgelet dictionary. The accuracy of identification in the case of head inclination by a certain angle for different ranges of possibilities of shifting the iris code is shown. Experimental results on the CASIA iris database show that the proposed method is effective and exhibits encouraging performance.

5

Human iris detection using fast cooperative modular neural nets and image decomposition

El-Bakry H. M.

Machine Graphics and Vision

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2002

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Vol. 11, No. 4

499-512

EN

In this paper, a combination of fast and cooperative modular neural nets to enhance the performance of the detection process is introduced. I have applied such approach successfully to detect human faces in cluttered scenes, [11]. Here, this technique is used to identify human irises automatically in a given image. Neural nets are used to test whether a window of 20x20 pixels contains an iris or not. The major difficulty in the learning process comes from the large database required for iris/non-iris images . A simple design for cooperative modular neural nets is presemted to solve this problem by dividing these data into three groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. Simulation results for the proposed algorithm show a good performance. Moreover, a powerful system for personal identification using iris detection is presented. Futhermore, faster iris detection is obtained through image decomposition into many sub-images and applying cross correlation in the frequency domain between each sub-image and the weights of the hidden layer.