Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

A Dorsal Hand Vein Recognition-based on Local Gabor Phase Quantization with Whitening Transformation

The hand vein pattern is a biometric feature in which the actual pattern is the shape of the vein network and its characteristics are the vein features. This paper investigates a new approach for dorsal hand vein pattern identification from grey level dorsal hand vein information. In this study Gabor filter quadrature pair is employed to compute locally in a window for every pixel position to extract the phase information. The phases of six frequency coefficients are quantized and it is used to form a descriptor code for the local region. These local descriptors are decorrelated using whitening transformation and a histogram is generated for every pixel which describes the local pattern.  Experiments are evaluated on North China University of Technology  dorsal hand vein image dataset with minimum distance classifier and the results are analyzed for recognition rate, run time and equal error rate. The proposed method gives 100 per cent recognition rate and 1 per cent EER for fusion of both left and right hands.Defence Science Journal, 2014, 64(2), pp. 159-167. DOI: http://dx.doi.org/10.14429/dsj.64.465

Neighborhood Defined Feature Selection Strategy for Improved Face Recognition in Different Sensor Modalitie

A novel feature selection strategy for improved face recognition in images with variations due to illumination conditions, facial expressions, and partial occlusions is presented in this dissertation. A hybrid face recognition system that uses feature maps of phase congruency and modular kernel spaces is developed. Phase congruency provides a measure that is independent of the overall magnitude of a signal, making it invariant to variations in image illumination and contrast. A novel modular kernel spaces approach is developed and implemented on the phase congruency feature maps. Smaller sub-regions from a predefined neighborhood within the phase congruency images of the training samples are merged to obtain a large set of features. These features are then projected into higher dimensional spaces using kernel methods. The unique modularization procedure developed in this research takes into consideration that the facial variations in a real world scenario are confined to local regions. The additional pixel dependencies that are considered based on their importance help in providing additional information for classification. This procedure also helps in robust localization of the variations, further improving classification accuracy. The effectiveness of the new feature selection strategy has been demonstrated by employing it in two specific applications via face authentication in low resolution cameras and face recognition using multiple sensors (visible and infrared). The face authentication system uses low quality images captured by a web camera. The optical sensor of the web camera is very sensitive to environmental illumination variations. It is observed that the feature selection policy overcomes the facial and environmental variations. A methodology based on multiple training images and clustering is also incorporated to overcome the additional challenges of computational efficiency and the subject\u27s non involvement. A multi-sensor image fusion based face recognition methodology that uses the proposed feature selection technique is presented in this dissertation. Research studies have indicated that complementary information from different sensors helps in improving the recognition accuracy compared to individual modalities. A decision level fusion methodology is also developed which provides better performance compared to individual as well as data level fusion modalities. The new decision level fusion technique is also robust to registration discrepancies, which is a very important factor in operational scenarios. Research work is progressing to use the new face recognition technique in multi-view images by employing independent systems for separate views and integrating the results with an appropriate voting procedure

Hand Vein Pattern Recognition using Natural Image Statistics

Biometrics is the science of identifying a person using physiological or behavioural characteristics. Hand vein pattern is a recent and unique biometric feature which is used for high secure authentication of individuals. The dorsal hand contains dorsal metacarpal veins, dorsal venous network, cephalic vein and basilic vein.  This paper presents an image descriptor which uses statistical structure of natural images. In this work, stack of natural image patches are used as filters and these transform an image into integer labels describing the small-scale appearance of the image. These labels are converted into histogram and it is used for further image analysis. The feature space contains binarized statistical image features. The proposed work is tested on NCUT dataset with state-of-the-art algorithms. The experimental results demonstrate that the proposed work outperforms of the state-of-the-art algorithms with the recognition rate of 99.80 per cent.Defence Science Journal, Vol. 65, No. 2, March 2015, pp.150-158, DOI:http://dx.doi.org/10.14429/dsj.65.731

High security human recognition system using iris images

In this paper, efficient biometric security technique for Integer Wavelet Transform based Human Recognition System (IWTHRS) using Iris images verification is described. Human Recognition using Iris images is one of the most secure and authentic among the other biometrics. The Iris and Pupil boundaries of an Eye are identified by Integro-Differential Operator. The features of the normalized Iris are extracted using Integer Wavelet Transform and Discrete Wavelet Transform. The Hamming Distance is used for matching of two Iris feature vectors. It is observed that the values of FAR, FRR, EER and computation time required are improved in the case of Integer Wavelet Transform based Human Recognition System as compared to Discrete Wavelet Transform based Human Recognition System (DWTHRS)

Multispectral Imaging For Face Recognition Over Varying Illumination

This dissertation addresses the advantage of using multispectral narrow-band images over conventional broad-band images for improved face recognition under varying illumination. To verify the effectiveness of multispectral images for improving face recognition performance, three sequential procedures are taken into action: multispectral face image acquisition, image fusion for multispectral and spectral band selection to remove information redundancy. Several efficient image fusion algorithms are proposed and conducted on spectral narrow-band face images in comparison to conventional images. Physics-based weighted fusion and illumination adjustment fusion make good use of spectral information in multispectral imaging process. The results demonstrate that fused narrow-band images outperform the conventional broad-band images under varying illuminations. In the case where multispectral images are acquired over severe changes in daylight, the fused images outperform conventional broad-band images by up to 78%. The success of fusing multispectral images lies in the fact that multispectral images can separate the illumination information from the reflectance of objects which is impossible for conventional broad-band images. To reduce the information redundancy among multispectral images and simplify the imaging system, distance-based band selection is proposed where a quantitative evaluation metric is defined to evaluate and differentiate the performance of multispectral narrow-band images. This method is proved to be exceptionally robust to parameter changes. Furthermore, complexity-guided distance-based band selection is proposed using model selection criterion for an automatic selection. The performance of selected bands outperforms the conventional images by up to 15%. From the significant performance improvement via distance-based band selection and complexity-guided distance-based band selection, we prove that specific facial information carried in certain narrow-band spectral images can enhance face recognition performance compared to broad-band images. In addition, both algorithms are proved to be independent to recognition engines. Significant performance improvement is achieved by proposed image fusion and band selection algorithms under varying illumination including outdoor daylight conditions. Our proposed imaging system and image processing algorithms lead to a new avenue of automatic face recognition system towards a better recognition performance than the conventional peer system over varying illuminations

An Enhanced Approach of Image Steganographic Using Discrete Shearlet Transform and Secret Sharing

               في الآونة الأخيرة، جعل الإنترنت المستخدمين قادرين على نقل الوسائط الرقمية بطريقة أسهل. على الرغم من هذه السهولة للإنترنت، إلا أنه قد تؤدي إلى العديد من التهديدات التي تتعلق بسرية محتويات الوسائط المنقولة مثل مصادقة الوسائط والتحقق من تكاملها. لهذه الأسباب ، يتم استخدام أساليب إخفاء البيانات والتشفير لحماية محتويات الوسائط الرقمية. في هذه الورقة البحثية ، تم اقتراح طريقة معززة لإخفاء المعلومات بالصور مع التشفير المرئي. يتم تشفير الشعار السري (صورة ثنائية) بالحجم (128 × 128) عن طريق تطبيق التشفير البصري (2 out 2 share) لتوليد مشاركتين سريتين. أثناء عملية التضمين ، يتم تقسيم الصورة غطاء RGB بحجم (512 × 512) إلى ثلاث طبقات (الأحمر والأخضر والأزرق). يتم تحويل الطبقة الزرقاء باستخدام التحويل Shearlet المتقطع للحصول على معاملاتها. يتم تضمين المشاركة السرية الأولى في معاملات الطبقة الزرقاء المحولة للحصول على صورة الاخفاء. في عملية الاستخراج ، يتم استخراج المشاركة السرية الأولى من معاملات الطبقة الزرقاء لصورة الاخفاء وثم يتم تطبيق عملية XOR عليها مع المشاركة السرية الثانية لإنشاء الشعار السري الأصلي. وفقًا للنتائج التجريبية ، فإن الطريقة المقترحة قد حققت افضل نسبة من عدم الوضوح لصورة الاخفاء بقدرة الحمولة الصافية تساوي (1 bpp). أصبح الشعار السري أكثر أمانًا باستخدام التشفير المرئي (2 out 2 share)  والمشاركة السرية الثانية كمفتاح خاص ايضاً.  Recently, the internet has made the users able to transmit the digital media in the easiest manner. In spite of this facility of the internet, this may lead to several threats that are concerned with confidentiality of transferred media contents such as media authentication and integrity verification. For these reasons, data hiding methods and cryptography are used to protect the contents of digital media. In this paper, an enhanced method of image steganography combined with visual cryptography has been proposed. A secret logo (binary image) of size (128x128) is encrypted by applying (2 out 2 share) visual cryptography on it to generate two secret share. During the embedding process, a cover red, green, and blue (RGB) image of size (512x512) is divided into three layers (red, green and blue). The blue layer is transformed using Discrete Shearlet Transform (DST) to obtain its coefficients. The first secret share is embedded at the coefficients of transformed blue layer to obtain a stego image. At extraction process, the first secret share is extracted from the coefficients of blue layer of the stego image and XORed with the second secret share to generate the original secret logo. According to the experimental results, the proposed method is achieved better imperceptibility for the stego image with the payload capacity equal to (1 bpp). In addition, the secret logo becomes more secured using (2 out 2 share) visual cryptography and the second secret share as a private key

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Manhattan Penalty Based Multi-Modal System for Facial Recognition

In this paper, a new approach for multimodal biometric techniques has been proposed. The new proposed approach utilizes data fusion techniques at score level of the system algorithm. Three different feature extraction algorithms have been chosen to extract features from the face image database of the individuals. These feature extraction algorithms (Principal Component Analysis, Local Binary Pattern, and Discrete wavelets transform) are used alongside K-nearest neighbor classifier to compute different score values for the same individual. These raw score values are fused together using a newly proposed data fusion techniques based on Manhattan distance penalty weighting. The proposed Manhattan penalty weighting penalizes an individual for scoring low points and further pushes it away from the potentially winning class before data fusion is conducted. The proposed approach was implemented on two public face recognition databases; ORL face database and YALE face database. The results of the proposed approach were evaluated using the recognition rates and receiver operating characteristics of the biometric classification systems. Experimental results have shown that the proposed multimodal system performs better than the unimodal system and other multimodal systems that used different data fusion rules (e.g. Sum Rule or Product Rule). In ORL database, the recognition rate of up to 97% can be obtained using the proposed techniqu