Sanjay N. Talbar

Dynamic iris localisation: A novel approach suitable for fake iris detection

In iris recognition system, accurate iris segmentation and localisation from eye image is the foremost important step. Success rate of any feature extraction algorithm of iris recognition systems is primarily decides by the performance of iris segmentation from an eye image. In the proposed method, the outer boundary of iris is calculated by tracing objects of various shape and structure. For inner iris boundary, two eye images of same subject at different intensities are compared with each other to detect the variation in pupil size. The variation in pupil size is also used for aliveness detection of iris. Thus, this approach is a very promising technique in making iris recognition systems more robust against fake-iris-based spoofing attempts. The algorithm is tested on Phoenix database of 384 images both eyes of 64 subjects. The success rate of accurate iris localisation from eye image is 99.48% with minimal loss of iris texture features in spatial domain as compared to all existing techniques. The processing time required is also comparable with existing techniques.

Motion Estimation Techniques for Digital Video Coding

The book deals with the development of a methodology to estimate the motion field between two frames for video coding applications. This book proposes an exhaustive study of the motion estimation process in the framework of a general video coder. The conceptual explanations are discussed in a simple language and with the use of suitable figures. The book will serve as a guide for new researchers working in the field of motion estimation techniques.

Local gray level S-curve transformation A generalized contrast enhancement technique for medical images

Most medical images suffer from inadequate contrast and brightness, which leads to blurred or weak edges (low contrast) between adjacent tissues resulting in poor segmentation and errors in classification of tissues. Thus, contrast enhancement to improve visual information is extremely important in the development of computational approaches for obtaining quantitative measurements from medical images. In this research, a contrast enhancement algorithm that applies gray-level S-curve transformation technique locally in medical images obtained from various modalities is investigated. The S-curve transformation is an extended gray level transformation technique that results into a curve similar to a sigmoid function through a pixel to pixel transformation. This curve essentially increases the difference between minimum and maximum gray values and the image gradient, locally thereby, strengthening edges between adjacent tissues. The performance of the proposed technique is determined by measuring several parameters namely, edge content (improvement in image gradient), enhancement measure (degree of contrast enhancement), absolute mean brightness error (luminance distortion caused by the enhancement), and feature similarity index measure (preservation of the original image features). Based on medical image datasets comprising 1937 images from various modalities such as ultrasound, mammograms, fluorescent images, fundus, X-ray radiographs and MR images, it is found that the local gray-level S-curve transformation outperforms existing techniques in terms of improved contrast and brightness, resulting in clear and strong edges between adjacent tissues. The proposed technique can be used as a preprocessing tool for effective segmentation and classification of tissue structures in medical images.

Iris Analysis for Biometric Recognition Systems

The book presents three most significant areas in Biometrics and Pattern Recognition. A step-by-step approach for design and implementation of Dual Tree Complex Wavelet Transform (DTCWT) plus Rotated Complex Wavelet Filters (RCWF) is discussed in detail. In addition to the above, the book provides detailed analysis of iris images and two methods of iris segmentation. It also discusses simplified study of some subspace-based methods and distance measures for iris recognition backed by empirical studies and statistical success verifications.

Multiresolution Analysis using Complex Wavelet and Curvelet Features for Content based Image Retrieval

In a typical content-based image retrieval (CBIR) system, retrieval results are a set of images sorted by feature similarities with respect to the query image. This paper demonstrates the comparative study of retrieval performance of CBIR system using real dual-tree DWT (R-DT-DWT), complex dual-tree DWT (C-DT-DWT) and Curvelet Transform. The experiments are carried out on Corel database of 1000 images database of 10 different classes with various similarity measures. The overall performance for Canberra distance was found to be better as compared to Minkowski and Manhattan distances. Experimental results indicate that the proposed method gives excellent average precision of 100% for Dinosaur class and 95% for roses class of images. Comparing the results and taking feature vector size into consideration, it may be better to opt for R-DT-DWT rather than C-DT-DWT or Curvelet features for feature extraction. But curvelet features contains more directional information at high frequencies and high frequency components provides better discrimination between images.

iMATCH: Image Matching and Retrieval for Digital Image Libraries

Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. It is increasingly evident that an image retrieval system has to be domain specific. In this paper, we present an algorithm for retrieving images with respect to a database consisting of difference class of images. The feature vectors used are DCT Coefficient arranged in a typical fashion. Image similarity is computed using k-means clustering followed by the modified chi-square distance measure.

A High Quality Steganographic Method Using Morphing

A new morphed steganographic algorithm is proposed in this paper. Image security is a challenging problem these days. Steganography is a method of hiding secret data in cover media. The Least Significant Bit is a standard Steganographic method that has some limitations. The limitations are less capacity to hide data, poor stego image quality, and imperceptibility. The proposed algorithm focuses on these limitations. The morphing concept is being used for image steganography to overcome these limitations. The PSNR and standard deviation are considered as a measure to improve stego image quality and morphed image selection, respectively. The stego keys are generated during the morphed steganographic embedding and extracting process. Stego keys are used to embed and extract the secret image. The experimental results, which are based on hiding capacity and PSNR, are presented in this paper. Our research contributes towards creating an improved steganographic method using image morphing. The experimental result indicates that the proposed algorithm achieves an increase in hiding capacity, stego image quality, and imperceptibility. The experimental results were compared with state of the art steganographic methods. Keywords—Morphed Steganography, Hiding Capacity, Imperceptibility, Stego Image Quality

Performance Evaluation of Block Matching Algorithms for Video Coding

A video sequence typically contains temporal redundancy; that is, two successive pictures are often very similar except for changes induced by object movement, illumination, camera movement, and so on. Motion estimation and compensation are used to reduce this type of redundancy in moving pictures. The block-matching algorithm (BMA) for motion estimation has proved to be very efficient in terms of quality and bit rate; therefore, it has been adopted by many standard video encoders. In this chapter, the basic principle of block matching motion estimation and compensation is introduced and fast motion search algorithms are addressed.

Iris Recognition Using Combination of Dual Tree Rotated Complex Wavelet and Dual Tree Complex Wavelet

The increasing requirement of security due to advances in information technologies, especially e-Commerce have led to rapid development of personnel identification /recognition systems based on biometric. A remarkable and important characteristic of the iris is the randomly distributed irregular texture details in all directions. In this paper, the authors have proposed a novel approach of feature extraction of iris image using 2D redundant rotated complex wavelet transform (RCWT) in combination with 2D Dual Trace Complex wavelet Transform(DT-CWT) to obtains the features in 12 different directions as against 3 and 6 directions in Discrete Wavelet Transform (DWT) and Complex Wavelet Transform (CWT) respectively. Iris features are obtained by computing energies and standard deviation of detailed coefficients in 12 directions. The sub-bands f RCWT are derived from sub-bands of CWT by using the suitable mapping rules. Canbera distance is used for matching. The results are obtained using DWT, CWT and combination of CWT and RCWT on UBIRIS database of 2400 images. The performance measure, ZeroFAR is reduced from 6.3 using DWT to 2.9 using the proposed method. The method is also computationally efficient as compared to Gabor Filters.

Comparative Analysis of Conventional, Real and Complex Wavelet Transforms for Face Recognition

recognition has been studied for many years in the context of biometrics and is one of the most successful applications of image analysis and understanding. Various methods, approaches and algorithms for recognition of human faces were proposed. In this paper, independent, comparative study of conventional discrete wavelet transform (DWT), real dual-tree discrete wavelet transform (R-DT-DWT), and complex dual-tree discrete wavelet transform (C-DT-DWT) based features for face recognition is carried out. In 2005, Delac et al. (26) presented an independent comparative study of PCA, ICA, and LDA on the FERET data set where it was concluded that no particular distance-metric combination is the best. In this paper we intend to bring further conclusions. Unlike the contribution by Delac et al., our conclusions are in context of DWT, R-DT-DWT, and C-DT-DWT. Moreover, these approaches are tested on nine different databases at different levels and under three different distance metrics, which allowed us to compare their performance independently. Our simulation results show that no particular distance-metric combination is the best across all standard benchmark face databases. However, the overall performance for city block distance measure was found to be better as compared to the Euclidean and cosine distance. Also, the performance for R-DT-DWT and C-DT-DWT based features were found equivalently efficient in many cases. So taking redundancy into consideration, it may be suggested to opt for R-DT-DWT for face efficient recognition.