Multilinear EigenECGs and FisherECGs for Individual Identification from Information Obtained by an Electrocardiogram Sensor

In this study, we present a third-order tensor-based multilinear eigenECG (MEECG) and multilinear Fisher ECG (MFECG) for individual identification based on the information obtained by an electrocardiogram (ECG) sensor. MEECG and MFECG are based on multilinear principal component analysis (MPCA) and multilinear linear discriminant analysis (MLDA) in the field of multilinear subspace learning (MSL), respectively. MSL directly extracts features without the vectorization of input data, while MSL extracts features without vectorizing the input data while maintaining most of the correlations shown in the original structure. In contrast with unsupervised linear subspace learning (LSL) techniques such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), it is less susceptible to small-data problems because it learns more compact and potentially useful representations, and it can efficiently handle large tensors. Here, the third-order tensor is formed by reordering the one-dimensional ECG signal into a two-dimensional matrix, considering the time frame. The MSL consists of four steps. The first step is preprocessing, in which input samples are centered. The second step is initialization, in which eigen decomposition is performed and the most significant eigenvectors are selected. The third step is local optimization, in which input data is applied by eigenvectors from the second step, and new eigenvectors are calculated using the applied input data. The final step is projection, in which the resultant feature tensors after projection are obtained. The experiments are performed on two databases for performance evaluation. The Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, and Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The experimental results revealed that the tensor-based MEECG and MFECG showed good identification performance in comparison to PCA and LDA of LSL.

[1]  Binh P. Nguyen,et al.  Robust Biometric Recognition From Palm Depth Images for Gloved Hands , 2015, IEEE Transactions on Human-Machine Systems.

[2]  Yudong Zhang,et al.  Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine , 2015, Entropy.

[3]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[4]  Joos Vandewalle,et al.  A Multilinear Singular Value Decomposition , 2000, SIAM J. Matrix Anal. Appl..

[5]  Yong Wang,et al.  ISAR Imaging of Rotating Target with Equal Changing Acceleration Based on the Cubic Phase Function , 2008, EURASIP J. Adv. Signal Process..

[6]  Jinyan Chen Gait Correlation Analysis Based Human Identification , 2014, TheScientificWorldJournal.

[7]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[8]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[9]  Hyun-Soo Choi,et al.  Biometric Authentication Using Noisy Electrocardiograms Acquired by Mobile Sensors , 2016, IEEE Access.

[10]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[11]  Anil K. Jain,et al.  Fingerprint Recognition of Young Children , 2017, IEEE Transactions on Information Forensics and Security.

[12]  Venu Govindaraju,et al.  Cognitive-Biometric Recognition From Language Usage: A Feasibility Study , 2017, IEEE Transactions on Information Forensics and Security.

[13]  Kil-Houm Park,et al.  Hierarchical Authentication Algorithm Using Curvature Based Fiducial Point Extraction of ECG Signals , 2017 .

[14]  Weihong Deng,et al.  Compressing Fisher Vector for Robust Face Recognition , 2017, IEEE Access.

[15]  Clemens Elster,et al.  Verification of humans using the electrocardiogram , 2007, Pattern Recognit. Lett..

[16]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[17]  Haifeng Hu,et al.  Face recognition using enhanced linear discriminant analysis , 2010 .

[18]  Sidan Du,et al.  Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification. , 2017, CNS & neurological disorders drug targets.

[19]  Sung Bum Pan,et al.  Biometrics System Technology Trends Based on Biosignal , 2017 .

[20]  Khashayar Khorasani,et al.  A neural-network appearance-based 3-D object recognition using independent component analysis , 2003, IEEE Trans. Neural Networks.

[21]  Raimondo Schettini,et al.  3D face detection using curvature analysis , 2006, Pattern Recognit..

[22]  Jieping Ye,et al.  Generalized Low Rank Approximations of Matrices , 2005, Machine Learning.

[23]  Chuang Lin,et al.  Orthogonal enhanced linear discriminant analysis for face recognition , 2016, IET Biom..

[24]  Winston A Hide,et al.  Big data: The future of biocuration , 2008, Nature.

[25]  Anil K. Jain,et al.  Incremental nonlinear dimensionality reduction by manifold learning , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[27]  Youming Zhang,et al.  On Biometrics With Eye Movements , 2017, IEEE Journal of Biomedical and Health Informatics.

[28]  Haiping Lu,et al.  MPCA: Multilinear Principal Component Analysis of Tensor Objects , 2008, IEEE Transactions on Neural Networks.

[29]  Patrick J. Flynn,et al.  A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition , 2006, Comput. Vis. Image Underst..

[30]  Brenda K. Wiederhold,et al.  ECG to identify individuals , 2005, Pattern Recognit..

[31]  Haiping Lu,et al.  A survey of multilinear subspace learning for tensor data , 2011, Pattern Recognit..