Investigation on advanced image search techniques

Content-based image search for retrieval of images based on the similarity in their visual contents, such as color, texture, and shape, to a query image is an active research area due to its broad applications. Color, for example, provides powerful information for image search and classification. This dissertation investigates advanced image search techniques and presents new color descriptors for image search and classification and robust image enhancement and segmentation methods for iris recognition. First, several new color descriptors have been developed for color image search. Specifically, a new oRGB-SIFT descriptor, which integrates the oRGB color space and the Scale-Invariant Feature Transform (SIFT), is proposed for image search and classification. The oRGB-SIFT descriptor is further integrated with other color SIFT features to produce the novel Color SIFT Fusion (CSF), the Color Grayscale SIFT Fusion (CGSF), and the CGSF+PHOG descriptors for image category search with applications to biometrics. Image classification is implemented using a novel EFM-KNN classifier, which combines the Enhanced Fisher Model (EFM) and the K Nearest Neighbor (KNN) decision rule. Experimental results on four large scale, grand challenge datasets have shown that the proposed oRGB-SIFT descriptor improves recognition performance upon other color SIFT descriptors, and the CSF, the CGSF, and the CGSF+PHOG descriptors perform better than the other color SIFT descriptors. The fusion of both Color SIFT descriptors (CSF) and Color Grayscale SIFT descriptor (CGSF) shows significant improvement in the classification performance, which indicates that various color-SIFT descriptors and grayscale-SIFT descriptor are not redundant for image search. Second, four novel color Local Binary Pattern (LBP) descriptors are presented for scene image and image texture classification. Specifically, the oRGB-LBP descriptor is derived in the oRGB color space. The other three color LBP descriptors, namely, the Color LBP Fusion (CLF), the Color Grayscale LBP Fusion (CGLF), and the CGLF+PHOG descriptors, are obtained by integrating the oRGB-LBP descriptor with some additional image features. Experimental results on three large scale, grand challenge datasets have shown that the proposed descriptors can improve scene image and image texture classification performance. Finally, a new iris recognition method based on a robust iris segmentation approach is presented for improving iris recognition performance. The proposed robust iris segmentation approach applies power-law transformations for more accurate detection of the pupil region, which significantly reduces the candidate limbic boundary search space for increasing detection accuracy and efficiency. As the limbic circle, which has a center within a close range of the pupil center, is selectively detected, the eyelid detection approach leads to improved iris recognition performance. Experiments using the Iris Challenge Evaluation (ICE) database show the effectiveness of the proposed method

Methods for iris classification and macro feature detection

This work deals with two distinct aspects of iris-based biometric systems: iris classification and macro-feature detection. Iris classification will benefit identification systems where the query image has to be compared against all identities in the database. By preclassifying the query image based on its texture, this comparison is executed only against those irises that are from the same class as the query image. In the proposed classification method, the normalized iris is tessellated into overlapping rectangular blocks and textural features are extracted from each block. A clustering scheme is used to generate multiple classes of irises based on the extracted features. A minimum distance classifier is then used to assign the query iris to a particular class. The use of multiple blocks with decision level fusion in the classification process is observed to enhance the accuracy of the method.;Most iris-based systems use the global and local texture information of the iris to perform matching. In order to exploit the anatomical structures within the iris during the matching stage, two methods to detect the macro-features of the iris in multi-spectral images are proposed. These macro-features typically correspond to anomalies in pigmentation and structure within the iris. The first method uses the edge-flow technique to localize these features. The second technique uses the SIFT (Scale Invariant Feature Transform) operator to detect discontinuities in the image. Preliminary results show that detection of these macro features is a difficult problem owing to the richness and variability in iris color and texture. Thus a large number of spurious features are detected by both the methods suggesting the need for designing more sophisticated algorithms. However the ability of the SIFT operator to match partial iris images is demonstrated thereby indicating the potential of this scheme to be used for macro-feature detection

Pengenalan Pola Posisi Iris Pada Sklera Mata dengan Metode Jaringan Saraf Konvolusional

Computer technology in this era become a human life adequate technology, one of the outstanding technology is Computer Vision. Computer Vision is a technique to duplicate human ability to understand image information, so that computer can recognize objects in the image as humans. When viewing objects in the form a pictures of cats, normal humans can understand easily, while computers are not. It’s because computers only see the image as a row of pixel values and the pixel data can only be processed by a computer using learning techniques. One of this method that is currently developed is the Convolutional Neural Network (CNN). In this research, CNN was used to classify the position of iris on the sclera which was subsequently implemented in a security lock system. The datasets that specified in this study were iris image data totaling 120 images taken from 10 subjects (humans) and divided into 2 types of classes, including right iris position and left iris position. Then, resize the image into 16x32 pixels. Next the color conversion is done in Grayscale format. CNN is designed using two layers of convolution with 3x3 sharpening and bluring filters accompanied by ReLU activation, two 2x2 Max-Pooling Layer processes, and Fully Connected Layer process with 2 hidden layers and 10 neurons in each layer. The results of the classification of the iris position in the eye sclera using the CNN method have an average accuracy 93%. These results were obtained from 15 tests with the number of training data 20, 40, 60, 80, and 100 images of iris. While the value of sharpening filters used for testing are [0 -1 0; -1 5 -1; 0 -1 0], [-1 -1 -1; -1 9 -1; -1 -1 -1], and [-2 -2 -2; -2 18 -2; -2 -2 -2]. The amount of training data and the variation of the filter value affects the accuracy of the classification results. The implementation of the iris position classification results on the eye sclera based on the pattern for the security lock system can also be done well. Selenoid key, led, and alarm can function according to the iris pattern given

Iris Data Indexing Method Using Biometric Features 1

Abstract A biometric system provides identification of an individual based on a unique feature or characteristic possessed by the individual. Among the available biometric identification system, Iris recognition is regarded as the most reliable and accurate one. Demands are increasing to deal with large scale databases in these applications. The Segmentation in boundary detection, edge Mapping, circular Hough Transform, extracting Region of interest (Eyelash and noise removal), circle detection. In a module of Person Identification system using Iris Recognition. The iris recognition system consists of a segmentation that is based on the Hough transform and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes and reflections. The extracted iris region was normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the data from Gabor filters was extracted and quantized to encode the unique pattern of the iris into a biometric template. To improve the efficiency of computational method and accuracy of classification, the Difference metric and subtraction method was employed. It was observed that this method classify the images with better accuracy. The Hamming distance was employed for classification of iris templates. The iris recognition is shown to be a reliable and accurate biometric technology. Keywords Gabor Filter Process, Image Recovery, Iris Biometric, Personal Verification I. Introduction The advances in Information technology and the increasing requirement of security issues have resulted in a rapid development of person identification based on biometrics. Biometric systems have been developed based on fingerprints, facial features, voice, hand geometry, handwriting, the retina, and the one concentrated and presented in this paper, the iris. Iris is regarded as the reliable and accurate technique because iris forms during gestation period itself and remains the same for the rest of one's life and it is unique for individuals. Iris is well protected and extremely difficult to modify. Biometric systems work by first capturing a sample of the feature, such as recording a digital sound signal for voice recognition, or taking a digital color image for face recognition, or taking a digital color image for iris recognition. The sample is then transformed using some sort of mathematical function into a biometric template. The biometric template will provide a normalized, efficient and highly discriminating representation of the feature, which can then be objectively compared with other templates in order to determine identity. Most biometric systems allow two modes of operation. An enrolment mode for adding templates to a database, and an identification mode, where a template is created for an individual and then a match is searched for in the database of pre-enrolled templates. A good biometric is characterized by use of a feature that is; highly unique -so that the chance of any two people having the same characteristic will be minimal, stable -so that the feature does not change over time, and be easily captured -in order to provide convenience to the user, and prevent misrepresentation of the feature

Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201