Recognition using SIFT and its Variants on Improved Segmented Iris

Iris is one of the most reliable biometric traits due to its stability and randomness. Iris is transformed to polar coordinates by the conventional recognition systems. They perform well for the cooperative databases, but the performance deteriorates for the non-cooperative irises. In addition to this, aliasing effect is introduced as a result of transforming iris to polar domain. In this thesis, these issues are addressed by considering annular iris free from noise due to eyelids. This thesis presents several SIFT based methods for extracting distinctive invariant features from iris that can be used to perform reliable matching between different views of an object or scene. After localization of the iris, Scale Invariant Feature Transform (SIFT) is used to extract the local features. The SIFT descriptor is a widely used method for matching image features. But SIFT is found out to be computationally very complex. So we use another keypoint descriptor, Speeded up Robust Features (SURF), which is found to be computationally more efficient and produces better results than the SIFT. Both SIFT and SURF has the problem of false pairing. This has been overcome by using Fourier transform with SIFT (called F-SIFT) to obtain the keypoint descriptor and Phase-Only Correlation for feature matching. F-SIFT was found to have better accuracy than both SIFT and SURF as the problem of false pairing is significantly reduced. We also propose a new method called S-SIFT where we used S Transform with SIFT to obtain the keypoint descriptor for the image and Phase-Only Correlation for the feature matching. In the thesis we provide a comparative analysis of these four methods (SIFT, SURF, F-SIFT, S-SIFT) for feature extraction in iris

Iris Recognition Using Scattering Transform and Textural Features

Iris recognition has drawn a lot of attention since the mid-twentieth century. Among all biometric features, iris is known to possess a rich set of features. Different features have been used to perform iris recognition in the past. In this paper, two powerful sets of features are introduced to be used for iris recognition: scattering transform-based features and textural features. PCA is also applied on the extracted features to reduce the dimensionality of the feature vector while preserving most of the information of its initial value. Minimum distance classifier is used to perform template matching for each new test sample. The proposed scheme is tested on a well-known iris database, and showed promising results with the best accuracy rate of 99.2%

Barcode Annotations for Medical Image Retrieval: A Preliminary Investigation

This paper proposes to generate and to use barcodes to annotate medical images and/or their regions of interest such as organs, tumors and tissue types. A multitude of efficient feature-based image retrieval methods already exist that can assign a query image to a certain image class. Visual annotations may help to increase the retrieval accuracy if combined with existing feature-based classification paradigms. Whereas with annotations we usually mean textual descriptions, in this paper barcode annotations are proposed. In particular, Radon barcodes (RBC) are introduced. As well, local binary patterns (LBP) and local Radon binary patterns (LRBP) are implemented as barcodes. The IRMA x-ray dataset with 12,677 training images and 1,733 test images is used to verify how barcodes could facilitate image retrieval.Comment: To be published in proceedings of The IEEE International Conference on Image Processing (ICIP 2015), September 27-30, 2015, Quebec City, Canad