Optimum Feature for Palmprint Image Authentication

Palm print authentications have become extensive research in recent years. Some research discussing palm print authentication emphasize on matching of two feature vectors of it. Problem faced by the research in this field is the sampling process. Different position of hand geometry results in different palm print image cause palm print to be unauthenticated. This research proposes an approach to solve the problem by first making image dimension using Multi-scale Wavelet Pyramid (MWP) to produce features represent palm print image. The next stage is feature matching by using Hamming Distance Similarity. Testing in several levels combination show that integration of level 1 and level 2 yields optimum feature. The evaluation result produce that MWP has faster and better performance accuracy up to 77.93% with threshold 4700

Iris Feature Extraction and Recognition Based on Wavelet-Based Contourlet Transform

AbstractIn view of the limitation of poor direction selectivity about 2-D wavelet transform and the problem of redundancy on contourlet transform, an iris texture feature extraction method based on wavelet-based contourlet transform (WBCT)for obtaining high quality features is proposed in the paper. Firstly, the preprocessed iris image is decomposed by WBCT, then calculating its energy, mean, standard deviation and Hu invariant moments of each subband of different scales and different directions, and taking them as the eigenvalues of iris image, finally, it tests on four iris image databases by using Euclidean distance. Experimental results show that the algorithm is simple and effective, and obtain better recognition performance

EKSTRAKSI FITUR BERBASIS WAVELET PADA SISTEM TEMU KEMBALI CITRA TEKSTUR

Pada penelitian ini diimplementasikan suatu sistem temu kembali citra tekstur dengan ekstraksi fitur berbasis wavelet. Ekstraksi fitur terdiri dari 2 proses utama, yaitu dekomposisi wavelet terhadap data citra, dan perhitungan energi dan deviasi standar terhadap koefisien-koefisien wavelet hasil dekomposisi. Penelitian ini bertujuan untuk mengetahui performance sistem temu kembali citra tekstur dengan menggunakan beberapa vektor fitur yang diekstrak dari beberapa level dekomposisi wavelet yang berbeda. Untuk uji coba, dibuat database citra menggunakan citra tekstur Brodatz. Filter yang digunakan pada proses dekomposisi diturunkan dari fungsi wavelet Daubechies4, dan untuk perhitungan kemiripan antara citra contoh dan citra dalam database digunakan Canberra distance. Hasil ujicoba menunjukkan bahwa performance terbaik dari sistem temu kembali citra tekstur adalah recall sebesar 92,5%, yang didapat dengan menggunakan vektor fitur yang diekstrak dari hasil dekomposisi wavelet sampai dengan level 2. Kata kunci: Temu kembali citra tekstur,Ttransformasi wavelet, Canberra distanc

Image Information Retrieval based on Edge Responses, Shape and Texture Features using Datamining Techniques

The present paper proposes a new technique that extracts significant structural, texture and local edge features from images. The local features are extracted by a steady local edge response that can sustain the presence of noise, illumination changes. The local edge response image is converted in to a ternary pattern image based on a local threshold. The structural features are derived by extracting shapes in the form of textons. The texture features are derived by constructing grey level co-occurrence matrix (GLCM) on the derived texton image. A new variant of K-means clustering scheme is proposed for clustering of images. The proposed method is compared with various methods of image retrieval based on data mining techniques. The experimental results on Wang dataset shows the efficacy of the proposed method over the other methods

Image Retrieval Using Content Information

Abstract: With the rapid development of the multimedia technologies such as compression and digital image sensor technologies, it is more and more necessary to build up a large database. Therefore robust and efficient methodology for image retrieval techniques has gained the attention of researchers. In the last few years the booming interest in the web images has paved a way to make people on content based Image retrieval techniques and also supporting systems for the retrieval process. In this paper, a general review of Content based image retrieval process by comparing with existing retrieval techniques along with experimental results and applications are presented. Some of the major challenges in the adaptation of content based image retrieval algorithms are also discussed

Improved methods for finger vein identification using composite median-wiener filter and hierarchical centroid features extraction

Finger vein identification is a potential new area in biometric systems. Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged because they reside underneath the skin of the finger and require a specific device to capture them. Research have been carried out in this field but there is still an unresolved issue related to low-quality data due to data capturing and processing. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. To address this issue, a new image enhancement and feature extraction methods were developed to improve finger vein identification. The image enhancement, Composite Median-Wiener (CMW) filter would improve image quality and preserve the edges of the finger vein image. Next, the feature extraction method, Hierarchical Centroid Feature Method (HCM) was fused with statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Each database was divided into training and testing sets. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. Both these methods produced accuracy as high as 97.64% for identification rate and 1.11% of equal error rate (EER) for measures verification rate. These showed that the accuracy of the proposed finger vein identification method is higher than the one reported in the literature. As a conclusion, the results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification