Ibrahim, Noor Salwani (2015) Reduced Set Kernel Principal Component Analysis (Rskpca) Algorithm for Palm Print Based Mobile Biometric System. Masters thesis, Universiti Sains Malaysia.
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Abstract
Kemunculan baru dimensi internet dan teknologi tanpa wayar telah membawa era baru dalam teknologi biometrik. Selain sistem biometrik dengan peranti statik, sistem biometrik mudah alih boleh dilaksanakan dan pendekatan ini membawa kepada pelaksanaan yang lebih cekap dan efisien. Dalam kajian ini, sistem biometrik mudah alih berasaskan tapak tangan telah dibangunkan. Walau bagaimanapun, untuk melaksanakan sistem biometrik mudah alih, masa pemprosesan dan penyimpanan yang cekap adalah faktor penting yang perlu dipertimbangkan.Dalam kajian ini, beberapa algoritma yang melibatkan pemprosesan ciri tapak tangan dinilai berdasarkan penggunaan masa dan memori yang optimum. Beberapa kaedah pemprosesan ciri termasuk Ruang Dikehendaki (ROI), Analisa Komponen Utama (PCA) dan Analisa Komponen Utama Kernel (KPCA) disiasat. Pendekatan baru dalam pengekstrakan ciri yang digelar Analisa Komponen Utama Kernel Set Dikurangi (RSKPCA) dicadangkan untuk mempercepatkan pemprosesan pengekstrakan ciri. RSKPCA yang dicadangkan menggunakan anggaran Kepadatan set Dikurangkan (RSDE) untuk menentukan matriks gram yang wajar. Hasilnya, RSKPCA hanya mengekstrak maklumat yang paling relevan dan penting dari set data. 2400 imej tapak tangan yang telah dikumpul daripada tiga jenis peranti Android mudah alih. Penilaian eksperimen menunjukkan bahawa RSKPCA yang dicadangkan mempunyai prestasi lebih baik berbanding ROI, PCA dan KPCA dengan Kadar Penerimaan Tulen (GAR) adalah lebih daripada 98% dan masa pemadanan kurang daripada 0.5s. Projek ini telah membuktikan bahawa pengektsrakan ciri menggunakan RSKPCA yang dicadangkan memberikan keputusan yang terbaik untuk sistem biometrik mudah alih berasaskan imej tapak tangan. ________________________________________________________________________________________________________________________ The emerging of internet and wireless dimension has brought a new era in biometrics technology. Instead of operating the biometric system with static biometric device, mobile biometric system can be implemented and this approach leads to more efficient and reliable implementation. In this study mobile biometric system based on palm print modality is developed. However, in order to execute mobile biometric system, efficient processing time and storage are some of the important factors that need to be considered. In this research, algorithms involving palm print feature processing are evaluated so as to obtain optimum time and memory consumption. Several feature processing methods including Region of Interest (ROI), Principal Component Analysis (PCA), and Kernel Principal Component Analysis (KPCA) are investigated. A new approach in feature extraction called Reduced-Set Kernel Principal Component Analysis (RSKPCA) is proposed to speed up the processing in feature extraction. The proposed RSKPCA employs a Reduced Set Density Estimate (RSDE) to define a weighted gram matrix. As a result, the RSKPCA only extracts the most relevant and important information from a dataset. 2400 palm print images which were collected from three types of android mobile are employed. Experimental evaluation shows that the proposed RSKPCA has better performance compared to the ROI, PCA and KPCA with the Genuine Acceptance Rates (GAR) is more than 98% and the matching time is less than 0.5s. In this project, it has been proven that the proposed RSKPCA as feature extraction gives the best result for mobile biometric system based on palm print.
| Item Type: | Thesis (Masters) |
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| Additional Information: | Full text is available at http://irplus.eng.usm.my:8080/ir_plus/institutionalPublicationPublicView.action?institutionalItemId=2103 |
| Subjects: | T Technology T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering > TK7800-8360 Electronics |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Thesis |
| Depositing User: | Mr Mohd Jasnizam Mohd Salleh |
| Date Deposited: | 12 Jun 2018 06:56 |
| Last Modified: | 12 Jun 2018 06:56 |
| URI: | http://eprints.usm.my/id/eprint/40766 |
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