A Review of Recent Advances in Identity Identification Technology Based on Biological Features

With the development of social informatization technology, the problems of individual information security are becoming serious. Nowadays identity identification has been required essentially in government and business field. In this paper, we summarize and analyze the identification principles and identification methods based on biometrics, including the present researches fingerprint, palmprint, iris, human face, vein, gait and signature, and make comparative analysis of the differences of the error recognition rate, stability, acquisition difficulty and counterfeiting degree. Finally, the prospects of biometric recognition technologies are discussed additionally.

[1]  J. Daugman.  High confidence recognition of persons by rapid video analysis of iris texture , 1995 .

[2]  B. H. Shekar,et al.  DCT-SVM-Based Technique for Off-line Signature Verification , 2014 .

[3]  Sharath Pankanti,et al.  On the Individuality of Fingerprints , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Xiaogang Wang,et al.  Deep Learning Face Representation from Predicting 10,000 Classes , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Jesús Francisco Vargas-Bonilla,et al.  Robustness of Offline Signature Verification Based on Gray Level Features , 2012, IEEE Transactions on Information Forensics and Security.

[7]  Richard Kissel,et al.  Glossary of Key Information Security Terms , 2014 .

[8]  N.A. Rahman,et al.  Retinal Identification , 2008, 2008 Cairo International Biomedical Engineering Conference.

[9]  Richard P. Wildes,et al.  A system for automated iris recognition , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[10]  Xiaogang Wang,et al.  DeepID3: Face Recognition with Very Deep Neural Networks , 2015, ArXiv.

[11]  Xiaogang Wang,et al.  Deep Learning Face Representation by Joint Identification-Verification , 2014, NIPS.

[12]  Jesús Francisco Vargas-Bonilla,et al.  Off-line signature verification based on grey level information using texture features , 2011, Pattern Recognit..

[13]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[14]  Xiaogang Wang,et al.  Deeply learned face representations are sparse, selective, and robust , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Liang Zhang.  Extraction of Direction Features in Fingerprint Image , 2014 .

[16]  Naoto Miura,et al.  Finger-vein authentication based on deformation-tolerant feature-point matching , 2016, Machine Vision and Applications.

[17]  Sanghamitra Das,et al.  Signature Verification Using Rough Set Theory Based Feature Selection , 2016 .

[18]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Lei Zhang,et al.  Three dimensional palmprint recognition , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[20]  Manuel G. Penedo,et al.  Personal authentication using digital retinal images , 2006, Pattern Analysis and Applications.

[21]  James A. McHugh,et al.  Automated fingerprint recognition using structural matching , 1990, Pattern Recognit..

[22]  Jiankun Hu,et al.  Global Ridge Orientation Modeling for Partial Fingerprint Identification , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Anil K. Jain,et al.  Handbook of Fingerprint Recognition , 2005, Springer Professional Computing.

[25]  Dario Maio,et al.  Direct Gray-Scale Minutiae Detection In Fingerprints , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Kuanquan Wang,et al.  Palmprint Texture Analysis Using Derivative of Gaussian Filters , 2006, 2006 International Conference on Computational Intelligence and Security.

[27]  Dewi Nasien.  Freeman chain code (fcc) representation in signature fraud detection based on nearest neighbourand artificial neural network (ann) classifiers. , 2014 .

[28]  Jing Wen,et al.  Off-Line Signature Verification Based on Local Structural Pattern Distribution Features , 2014, CCPR.

[29]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[30]  Farshid Hajati,et al.  Offline signature verification using geodesic derivative pattern , 2014, 2014 22nd Iranian Conference on Electrical Engineering (ICEE).

[31]  Eui Chul Lee,et al.  New Finger Biometric Method Using Near Infrared Imaging , 2011, Sensors.

[32]  Haifeng Hu,et al.  Enhanced Gabor Feature Based Classification Using a Regularized Locally Tensor Discriminant Model for Multiview Gait Recognition , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[33]  Yu Qiao,et al.  A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.

[34]  Ajay Kumar,et al.  Contactless 3D Fingerprint Identification , 2018, Advances in Computer Vision and Pattern Recognition.

[35]  Stephen D. Wolthusen,et al.  Visible-Spectrum Biometric Retina Recognition , 2008, 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[36]  Ming Yang,et al.  DeepFace: Closing the Gap to Human-Level Performance in Face Verification , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Pooja Choudhary,et al.  Offline Signature Verification in Punjabi based on SURF Features and Critical Point Matching using HMM , 2015 .

[38]  Ajay Kumar,et al.  Contactless and partial 3D fingerprint recognition using multi-view deep representation , 2018, Pattern Recognit..

[39]  Euntai Kim,et al.  A probabilistic image-weighting scheme for robust silhouette-based gait recognition , 2012, Multimedia Tools and Applications.

[40]  Zhenan Sun,et al.  A Lightened CNN for Deep Face Representation , 2015, ArXiv.