Email this article Survei Penelitian Pengenalan Pola dalam Identifikasi Biometrik
Abstract
Pengenalan pola memainkan peranan yang penting dalam identifikasi
biometrik. Hal ini dikarenakan pengenalan pola dalam identifikasi
biometrik membantu pihak berwenang dalam mengungkap identitas
seorang kriminal. Pengenalan pola identifikasi biometrik dalam image
processing mencakup pengenalan pola wajah, geometri dari sebuah
tangan, iris dan retina dari organ mata, sklera mata, pembuluh darah,
tanda kulit dan rambut tubuh. Pengenalan pola identifikasi biometrik
membutuhkan metode pengenalan pola yang akurat, pemilihan tahap pra
proses dan metode klasifikasi yang sesuai. Pada survei paper ini dibahas
mengenai beberapa metode tahap pra proses seperti Averaging Filter,
Histogram, Desaturation, Binerisation dan Image Alignment. Metode
pengenalan pola yang dibahas pada paper ini adalah Gabor Features,
Local Binary Pattern, Local Gabor Binary Pattern dan Haar Wavelet
Transform. Sedangkan metode klasifikasi yang dibahas adalah Euclidean
distance, Chi-square distance dan Histogram Matching. Agar dapat
memberikan hasil terbaik, setiap sistem pengenalan pola tidak dapat
menggunakan metode yang sama untuk mengenali pola identifikasi
biometrik yang berbeda. Dibutuhkan penelitian dalam penggunaan
metode pra proses, ekstraksi fitur dan klasifikasi untuk setiap identifikasi
biometrik yang ingin dikenali polanya.
Keywords
Full Text:
PDFReferences
Anil K. Jain, et al., Handbook of Biometrics, Springer, 2008.
Agarwal, Sugandha, et al. "A Comparative Study of Facial, Retinal, Iris and Sclera Recognition Techniques.", IOSR Journal of Computr Engineering, Vol. 16, p. 47-52, 2014.
The U.S. Attorney’s Office, Central District of California, Release No. 08-074, “Ex-marine Guilty of Using Drugs and Force to Have Sex with Young Girls in Cambodia,” 29 May 2008. Available: http://www.justice.gov/usao/cac/Pressroom/pr2008/074.html
United States v. Pepe, Case No. 07-168-DSF. Trial transcript, May 5, 2008.
H. Zhang, C. Tang, A.W.K. Kong and N. Craft, “Matching vein patterns from color images for forensic investigation”, BTAS, 2012, pp. 77-84.
A. Nurhudatiana, A.W.K. Kong, K. Matinpour, S.Y. Cho and Noah Craft, “Fundamental statistics of relatively permanent pigmented or vascular skin marks”, Proc. of International Joint Conference on Biometrics, 2011, pp. 1-6.
C. Tang, A.W.K. Kong and Noah Craft, “Uncovering vein patterns from color skin images for forensic analysis”, CVPR, 2011, pp. 665-672.
A. Nurhudatiana et al., "The Individuality of Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM) in Independently and Uniformly Distributed Patterns," in IEEE Transactions on Information Forensics and Security, vol. 8, no. 6, pp. 998-1012, June 2013.
A. Nurhudatiana and A. W. K. Kong, "On Criminal Identification in Color Skin Images Using Skin Marks (RPPVSM) and Fusion With Inferred Vein Patterns," in IEEE Transactions on Information Forensics and Security, vol. 10, no. 5, pp. 916-931, May 2015.
H. Su and A. W. K. Kong, "A Study on Low Resolution Androgenic Hair Patterns for Criminal and Victim Identification," in IEEE Transactions on Information Forensics and Security, vol. 9, no. 4, pp. 666-680, April 2014.
M. R. Islam, F. K. S. Chan and A. W. K. Kong, "A Preliminary Study of Lower Leg Geometry as a Soft Biometric Trait for Forensic Investigation," Pattern Recognition (ICPR), 2014 22nd International Conference on, Stockholm, 2014, pp. 427-431.
F. K. S. Chan and A. W. K. Kong, "Using Leg Geometry to Align Androgenic Hair Patterns in Low Resolution Images for Criminal and Victim Identification," Pattern Recognition (ICPR), 2014 22nd International Conference on, Stockholm, 2014, pp. 495-500.
F. K. S. Chan and A. W. K. Kong, "Using Hair Follicles with Leg Geometry to Align
Androgenic Hair Patterns," Intelligence and Security Informatics Conference (EISIC), 2015 European, Manchester, 2015, pp. 137-140.
R. Lionnie, I.K. Timotius and I. Setyawan, “Performance Comparison of Several Pre-Processing Methods in a Hand Gesture Recognition System based on Nearest Neighbor for Different Background Conditions,” ITB ICT Journal, 2012, Vol. 6, No. 2, pp. 184-195.
R. Lionnie, I. K. Timotius and I. Setyawan, "An analysis of edge detection as a feature extractor in a hand gesture recognition system based on nearest neighbor," Electrical Engineering and Informatics (ICEEI), 2011 International Conference on, Bandung, 2011, pp. 1-4.
P. S. Sanjekar and P. S. Dhabe, "Fingerprint verification using haar wavelet," Computer Engineering and Technology (ICCET), 2010 2nd International Conference on, Chengdu, 2010, pp. V3-361-V3-365.
R. Lionnie, I. K. Timotius and I. Setyawan, “Penggunaan Transformasi Wavelet dalam Sistem Pengenalan Isyarat Tangan dengan Beberapa Kombinasi Pra-Proses,” TECHNe, Jurnal Ilmiah Elektronika vol. 10, no. 2, October 2011, FTEK UKSW
C. F. Liew and T. Yairi, "A comparison study of feature spaces and classification methods for facial expression recognition," Robotics and Biomimetics (ROBIO), 2013 IEEE International Conference on, Shenzhen, 2013, pp. 1294-1299.
G. Heumer, H. B. Amor, M. Weber and B. Jung, "Grasp Recognition with uncalibrated Data Gloves - A Comparison of Classification Methods," 2007 IEEE Virtual Reality Conference, Charlotte, NC, 2007, pp. 19-26.
Gonzalez, Rafael C., and Richard E. Woods., Digital Image Processing 2nd ed, Prentice Hall, 2002.
N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), San Diego, CA, USA, 2005, pp. 886-893 vol. 1.
Anil K. Jain, et al., Handbook of Face Recognition, Springer Verlag, London, 2011.
J. G. Daugman, "High confidence visual recognition of persons by a test of statistical independence," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, Nov 1993.
Kong, Adams Wai-Kin. "An analysis of Gabor detection." Image Analysis and Recognition. Springer Berlin Heidelberg, 2009. 64-72.
Paul S. Addison, The Illustrated Wavelet Transform Handbook, IOP Publishing, 2002.
Ojala, Timo, Matti Pietikäinen, and David Harwood. "A comparative study of texture
measures with classification based on featured distributions." Pattern recognition 29.1 (1996): 51-59.
Ojala, Timo, Matti Pietikäinen, and Topi Mäenpää. "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns."Pattern Analysis and Machine Intelligence, IEEE Transactions on 24.7 (2002): 971-987.
D. Huang, C. Shan, M. Ardabilian, Y. Wang and L. Chen, "Local Binary Patterns and Its Application to Facial Image Analysis: A Survey," in IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 41, no. 6, pp. 765-781, Nov. 2011.
J. Ruiz-del-Solar and J. Quinteros, “Illumination compensation and normalization in
eigenspace-based face recognition: A comparative study of different pre-processing
approaches,” Pattern Recog. Lett., vol. 29, no. 14, pp. 1966–1979, 2008.
G. Bai, Y. Zhu, and Z. Ding, “A hierarchical face recognition method based on local binary pattern,” in Proc. Congr. Image Signal Process., May 2008, pp. II: 610–614.
X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” in Proc. Anal. Model. Faces Gestures, 2007, pp. 168–182.
T. Ahonen and M. Pietik¨ainen, “Soft histograms for local binary patterns,” in Proc. Fin. Signal Process. Symp., Oulu, Finland, 2007.
S. Liao and S. Z. Li, “Learning multi-scale block local binary patterns for face recognition,” in Proc. Int. Conf. Biometrics, 2007, pp. 828–837.
L. Wolf, T. Hassner, and Y. Taigman, “Descriptor based methods in the wild,” in Proc. ECCV Workshop Faces ‘Real-Life’ Images: Detection, Alignment, Recog., Marseille, France, 2008.
L. Paulhac, P. Makris, and J.-Y. Ramel, “Comparison between 2D and 3D local binary pattern methods for characterization of three-dimensional textures,” in Proc. Int. Conf. Image Anal. Recog., 2008, pp. 670–679.
G. Zhao and M. Pietik¨ainen, “Dynamic texture recognition using local binary patterns with an application to facial expressions,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6, pp. 915–928, Jun. 2007.
D. Huang, G. Zhang, M. Ardabilian, Y. Wang, and L. Chen, “3D face recognition using distinctiveness enhanced facial representations and local feature hybrid matching,” in Proc. IEEE Int. Conf. Biometrics: Theor., Appl. Syst., Washington, DC, Sep. 2010.
Wenchao Zhang, Shiguang Shan, Wen Gao, Xilin Chen and Hongming Zhang, "Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition,"Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005, pp. 786-791 Vol. 1.
DOI: http://dx.doi.org/10.22441/incomtech.v7i1.1162
Publisher Address:
Magister Teknik Elektro, Universitas Mercu Buana
Jl. Meruya Selatan 1, Jakarta 11650
Phone (021) 31935454/ 31934474
Fax (021) 31934474
Email: [email protected]
Website of Master Program in Electrical Engineering
http://mte.pasca.mercubuana.ac.id
pISSN: 2085-4811
eISSN: 2579-6089
Jurnal URL: http://publikasi.mercubuana.ac.id/index.php/Incomtech
Jurnal DOI: 10.22441/incomtech
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional
The Journal is Indexed and Journal List Title by:
