Bidirectional PCA with assembled matrix distance metric for image recognition

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Palmprint identification using restricted fusion

2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Identifikasi Telapak Tangan Menggunakan 2DPCA Plus PCA Dan K-Nearest Neighbour

Biometrik merupakan pengenalan seseorang berdasarkan anatomi ataupun karakteristik tingkah laku. Identifikasi telapak tangan sebagai teknik biometrik memiliki kelebihan yaitu membutuhkan resolusi citra yang kecil, biaya alat yang murah, tidak bersifat intrusif, serta memiliki tingkat akurasi yang tinggi. Paper ini akan membahas identifikasi telapak tangan menggunakan metode 2DPCA plus PCA. Principal Component Analysis (PCA) merupakan teknik ekstraksi fitur yang memiliki kelebihan dari segi kompresi data. Metode 2 Dimensional Principal Component Analysis (2DPCA) memiliki kelebihan dari PCA dari segi akurasi dan kompleksitas waktu, namun memiliki kekurangan yaitu membutuhkan banyak koefisien dalam menyimpan data. Penggabungan kedua metode tersebut menghasilkan metode yaitu 2DPCA plus PCA. Pada percobaan, klasifikasi dilakukan dengan menggunakan K-Nearest Neighbour (KNN). Analisis dilakukan dengan membandingkan performansi metode 2DPCA plus PCA dengan metode lainnya yaitu 2DPCA dan PCA

Identifikasi Telapak Tangan Menggunakan 2DPCA Plus PCA dan K-Nearest Neighbour

Biometrik merupakan pengenalan seseorang berdasarkan anatomi ataupun karakteristik tingkah laku. Identifikasi telapak tangan sebagai teknik biometrik memiliki kelebihan yaitu membutuhkan resolusi citra yang kecil, biaya alat yang murah, tidak bersifat intrusif, serta memiliki tingkat akurasi yang tinggi. Paper ini akan membahas identifikasi telapak tangan menggunakan metode 2DPCA plus PCA. Principal Component Analysis (PCA) merupakan teknik ekstraksi fitur yang memiliki kelebihan dari segi kompresi data. Metode 2 Dimensional Principal Component Analysis (2DPCA) memiliki kelebihan dari PCA dari segi akurasi dan kompleksitas waktu, namun memiliki kekurangan yaitu membutuhkan banyak koefisien dalam menyimpan data. Penggabungan kedua metode tersebut menghasilkan metode yaitu 2DPCA plus PCA. Pada percobaan, klasifikasi dilakukan dengan menggunakan K-Nearest Neighbour (KNN). Analisis dilakukan dengan membandingkan performansi metode 2DPCA plus PCA dengan metode lainnya yaitu 2DPCA dan PCA

The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Fusion of face and iris biometrics in security verification systems.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban, 2016.Abstract available in PDF file

Biometric Applications Based on Multiresolution Analysis Tools

This dissertation is dedicated to the development of new algorithms for biometric applications based on multiresolution analysis tools. Biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual\u27s identity. Biometrics can measure physiological, behavioral, physical and chemical characteristics of an individual. Physiological characteristics are based on measurements derived from direct measurement of a part of human body, such as, face, fingerprint, iris, retina etc. We focussed our investigations to fingerprint and face recognition since these two biometric modalities are used in conjunction to obtain reliable identification by various border security and law enforcement agencies. We developed an efficient and robust human face recognition algorithm for potential law enforcement applications. A generic fingerprint compression algorithm based on state of the art multiresolution analysis tool to speed up data archiving and recognition was also proposed. Finally, we put forth a new fingerprint matching algorithm by generating an efficient set of fingerprint features to minimize false matches and improve identification accuracy. Face recognition algorithms were proposed based on curvelet transform using kernel based principal component analysis and bidirectional two-dimensional principal component analysis and numerous experiments were performed using popular human face databases. Significant improvements in recognition accuracy were achieved and the proposed methods drastically outperformed conventional face recognition systems that employed linear one-dimensional principal component analysis. Compression schemes based on wave atoms decomposition were proposed and major improvements in peak signal to noise ratio were obtained in comparison to Federal Bureau of Investigation\u27s wavelet scalar quantization scheme. Improved performance was more pronounced and distinct at higher compression ratios. Finally, a fingerprint matching algorithm based on wave atoms decomposition, bidirectional two dimensional principal component analysis and extreme learning machine was proposed and noteworthy improvements in accuracy were realized

Biometric face recognition using multilinear projection and artificial intelligence

PhD ThesisNumerous problems of automatic facial recognition in the linear and multilinear subspace learning have been addressed; nevertheless, many difficulties remain. This work focuses on two key problems for automatic facial recognition and feature extraction: object representation and high dimensionality. To address these problems, a bidirectional two-dimensional neighborhood preserving projection (B2DNPP) approach for human facial recognition has been developed. Compared with 2DNPP, the proposed method operates on 2-D facial images and performs reductions on the directions of both rows and columns of images. Furthermore, it has the ability to reveal variations between these directions. To further improve the performance of the B2DNPP method, a new B2DNPP based on the curvelet decomposition of human facial images is introduced. The curvelet multi- resolution tool enhances the edges representation and other singularities along curves, and thus improves directional features. In this method, an extreme learning machine (ELM) classifier is used which significantly improves classification rate. The proposed C-B2DNPP method decreases error rate from 5.9% to 3.5%, from 3.7% to 2.0% and from 19.7% to 14.2% using ORL, AR, and FERET databases compared with 2DNPP. Therefore, it achieves decreases in error rate more than 40%, 45%, and 27% respectively with the ORL, AR, and FERET databases. Facial images have particular natural structures in the form of two-, three-, or even higher-order tensors. Therefore, a novel method of supervised and unsupervised multilinear neighborhood preserving projection (MNPP) is proposed for face recognition. This allows the natural representation of multidimensional images 2-D, 3-D or higher-order tensors and extracts useful information directly from tensotial data rather than from matrices or vectors. As opposed to a B2DNPP which derives only two subspaces, in the MNPP method multiple interrelated subspaces are obtained over different tensor directions, so that the subspaces are learned iteratively by unfolding the tensor along the different directions. The performance of the MNPP has performed in terms of the two modes of facial recognition biometrics systems of identification and verification. The proposed supervised MNPP method achieved decrease over 50.8%, 75.6%, and 44.6% in error rate using ORL, AR, and FERET databases respectively, compared with 2DNPP. Therefore, the results demonstrate that the MNPP approach obtains the best overall performance in various learning scenarios