Image quality assessment for iris biometric

The ability to identify individuals based on their iris is known as iris recognition. Over the past decade iris recognition has garnered much attention because of its strong performance in comparison with other mainstream biometrics such as fingerprint and face recognition. Performance of iris recognition systems is driven by application scenario requirements. Standoff distance, subject cooperation, underlying optics, and illumination are a few examples of these requirements which dictate the nature of images an iris recognition system has to process. Traditional iris recognition systems, dubbed "stop and stare", operate under highly constrained conditions. This ensures that the captured image is of sufficient quality so that the success of subsequent processing stages, segmentation, encoding, and matching are not compromised. When acquisition constraints are relaxed, such as for surveillance or iris on the move, the fidelity of subsequent processing steps lessens.;In this dissertation we propose a multi-faceted framework for mitigating the difficulties associated with non-ideal iris. We develop and investigate a comprehensive iris image quality metric that is predictive of iris matching performance. The metric is composed of photometric measures such as defocus, motion blur, and illumination, but also contains domain specific measures such as occlusion, and gaze angle. These measures are then combined through a fusion rule based on Dempster-Shafer theory. Related to iris segmentation, which is arguably one of the most important tasks in iris recognition, we develop metrics which are used to evaluate the precision of the pupil and iris boundaries. Furthermore, we illustrate three methods which take advantage of the proposed segmentation metrics for rectifying incorrect segmentation boundaries. Finally, we look at the issue of iris image interoperability and demonstrate that techniques from the field of hardware fingerprinting can be utilized to improve iris matching performance when images captured from distinct sensors are involved.

The quality of biometrie data has a strong relationship with the performance of a face recognition system. The accuracy of automated face recognition systems is greatly affected by various quality factors, such as illumination, contrast, brightness, and blur. Therefore, an effective method is needed, which can characterize the quality of the facial image by fusing different quality measures into a single quality score. In this paper, we propose a novel quality estimation method based on linear regression analysis, to model the relationship between different quality factors and corresponding face recognition performance. A practical set of quality measures is used to estimate the quality scores. The linear regression model adjusts the weight of different quality factors according to their impact on recognition performance. The facial features are extracted using Local Binary Pattern (LBP) and k-nearest neighbor (KNN) classifier is used for the classification purpose. The prediction scores generated from the model is a strong indicator of the overall quality of a facial image. This model has many applications, for example, saving the processing time and improving the face recognition accuracy during enrollment processes by discarding poor quality images. The residual error of the regression model is 4.29%, and considering 0 and ±1 error between original response value and the prediction value results in a very high accuracy of 94.06%.

Blur detection is aimed at estimating the probability of each pixel being blurred or non-blurred in an image affected by motion or defocus blur. This task has gained considerable attention due to its promising application fields in computer vision. Accurate differentiation of anomalous regions (including the sharp but homogeneous regions and pseudo-sharp backgrounds) and motion-blurred regions are main challenges in blur detection, in which both conventional and recently developed blur detection methods have limited performance and low time efficiency. To address these issues, this paper develops an accurate and fast blur detection method for both motion and defocus blur using a new end-to-end deep neural network. First, a novel multi-input multi-loss encoder-decoder network (M-shaped) is proposed to learn rich hierarchical representations related to blur. Then, to resolve the problem shows that blur degree is susceptible to scales, we construct a pyramid ensemble model (PM-Net) consisting of different scales of M-shaped subnets and a unified fusion layer. The experiments demonstrate that the proposed PM-Net can accurately handle those challenging scenarios with anomalous regions for both defocus and motion blur. Our method performs better than previous state-of-the-art methods. It achieves the $F_{1}$ -score of 0.893 for only defocus blur and 0.884 for joint motion and defocus blur, both of which significantly surpass previous methods on the benchmark BDD dataset. We also test our PM-Net on another public CDD dataset composed of challenging defocused images. The proposed method also outperforms other published methods with an $F_{1}$ -score of 0.885. In addition, our proposed method is hundreds of times faster (millisecond) than other state-of-the-art methods (second). Moreover, our experiments also demonstrate that the PM-Net is robust to noise and has a good generalization property.

The texture of the iris is commonly represented as an iris code in iris recognition systems. While several approaches have been presented for generating iris codes, relatively few comparison techniques have been proposed. In this paper, we take advantage of the availability of several frames from an iris video to create a single optimized iris code. This is achieved by performing both row-wise and column-wise optimization of iris codes. Inconsistent bits are accurately detected and masked in the final iris code. Our experiments demonstrate that by exploiting variations within the comparison scores of different rows and columns of N frames, we are able to derive the number of consistent bits in the final iris code thereby resulting in significant improvement in recognition performance. We compare our algorithm with well-known methods, namely, Fragile bit masking, Signal fusion and, two Score Fusion techniques. Experimental results on a dataset of 986 iris videos show that the proposed method is encouraging and comparable to the best algorithms in the current literature. To our knowledge, this is the first work that makes use of the best rows and columns from different frames in an iris video to improve performance.

The performance of a speech recognition system may be degraded even without any background noise because of the linear or non-linear distortions incurred by recording devices or reverberations. One of the well-known approaches to reduce this channel distortion is feature mapping which maps the distorted speech feature to its clean counterpart. The feature mapping rule is usually trained based on a set of stereo data which consists of the simultaneous recordings obtained in both the reference and target conditions. In this paper, we propose a novel approach to speech feature sequence mapping based on the switching linear dynamic transducer (SLDT). The proposed algorithm enables us a sequence-to-sequence mapping in a systematic way, instead of the traditional vector-to-vector mapping. The proposed approach is applied to compensate channel distortion in speech recognition and shows improvement in recognition performance.

Optimal Speech feature extraction which tries to acquire a parametric illustration of an input speech signal plays a vital role in the overall performance of an Automatic Speech Recognition (ASR) system. A good feature extraction technique along with feature selection algorithm should capture the important features of the signal and also should reject some irrelevant features. Feature selection is a critical task which can influence the performance of pattern classification and recognition system. In this paper, we have presented a hybrid evolutionary algorithm called Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) for optimal feature extraction and selection strategy. The proposed method consists of three stages, preprocessing, feature extraction and selection, and recognition. Initially, the preprocessing is done by wiener filter to reduce the noise level. Next, we extract eight type of statistical and acoustic features in the feature stage. The optimal set of extracted features is selected by using hybrid ABC-PSO algorithm. Finally, these optimized features are used for training the Support Vector Machine (SVM) classifier and based on these optimized features of the given input speech signal the corresponding text is displayed as the output. The proposed ASR is implemented on the MATLAB working platform and the experimental results show that overall performance of the system is high and the proposed hybrid algorithm is best suited for speech recognition.

This paper describes the work done in improving the performance of Tamil speech recognition system by using Time Scale Modification (TSM) and Vocal Tract Length Normalization (VTLN) techniques. The speech recognition system for Tamil language was developed using a new approach of text independent speech segmentation, with a phoneme based language model for recognition. There is degradation in the performance of speech recognition due to variations in the speaking rate and vocal tract shape among different speakers. In order to improve the performance of speech recognition system, both TSM and VTLN normalization techniques were used in this work. The TSM was implemented using the Phase vocoder approach and the VTLN was implemented using speaker specific bark/mel scale in bark/mel domain. The performance of Tamil speech recognition system was improved by performing both TSM and VTLN normalization techniques.

Combined speech enhancement and auditory modelling for robust distributed speech recognition

The role of biometrics, as an integral component of security infrastructures, has been firmly established in today's world. Its ability to accurately identify people from a distance has served as an important life-saving tool in the arsenalof our men and women in uniform, out in war theaters; it has also served as an effective forensics tool for our police force and off-late, is being increasingly adopted to secure sensitive transactions such as those with financial institutions.Traditionally, fingerprint patterns and facial photographs have held sway over identity veri fication tools; the former, because of its uniqueness and the latter because of its ubiquity. However, over the past decade, iris patterns have alsocome to the fore because of the combined advantage of uniqueness across the populace as well as the ability to image these patterns from a distance. Today most commercial iris acquisition systems report recognition accuracies upwards of 90%, over entire populations. However, the caveat is that these systems areconstrained to operate very close to the user, owing to the challenges involved in achieving sufficient resolution over the iride texture from large distances. This dissertation aims to push the boundaries of iris imaging devices and in-troduces theoretical and practical considerations in designing a novel long range iris recognition system. The aim is to design a system capable of acquiring enrollment quality iris images, similar in quality to those captured by short range systems such as IriShield, LG, PIER, HIIDE systems, at much larger stand-off distances. The challenges involved in acquiring images with sufficient spatialresolution, when subjects are too far out from the optimal acquisition range, are explored and potential solutions are described. First, we describe the design methodology behind creating a long range iris system, to aid the reader inunderstanding the key design considerations when using available commercial o -the-shelf hardware. Competing objectives such as achieving high magnifi cation while maintaining sufficient depth of field, using a sensor with large pixel pitch for low noise performance while resolving the required patterns, etc. are discussed. As a result of this work, the rst long range iris acquisition system of its kind, capable of acquiring enrollment iris images from up to 9mand matching up to 12m, has been designed and built. We show the system, present lessons learned during the design process as well as empirical results for the imaging performance. The latter half of the dissertation develops aniris texture enhancement algorithm for restoring degraded iris images - specifically noisy, low-resolution and out-of-focus/blurred images, which are often encountered in the real world. The method is developed and evaluated usingsynthesized degraded images from the NIST Iris Challenge Evaluation (ICE) database. A signifi cant improvement in verification performance is also seen, when using this enhancement method, on actual degraded images captured with the proposed long range iris system. The proposed enhancement method is modeled as a linear regression problem that exploits the sparsity of iris textures inan over-complete dictionary. Improvements in iris recognition performance using the enhanced textures are studied in the latter half of this dissertation. In summary, this work enhances and exceeds the capabilities of current iris imaging systems for the explicit purpose of accurately identifying threats using iride patterns, from distances larger than are currently possible. This will serve as anecessary tool in the arsenal of security establishments to identify threats, save lives and to protect our freedoms for generations to come.

The Iris recognition is commonly used as a security system due to its robustness against imposters. Iris datasets are huge and hence those datasets occupy more space. Iris image compression has become an important part of better performance like speed and data storage. The portable Iris system is in huge demand. That portable systems need to transmit the iris images through a very small bandwidth channel. To reduce the time for transferring a huge number of data over small bandwidth channel, iris file can be compressed to some extent to minimize the size. Another problem is that when an image is captured, it captures some noise that disturbs the recognition performance, so, denoising is required for noise-free images. This paper separately analyzes the impact of wavelet compression along with denoising on iris images. The compression analysis is done using Embedded Zero Tree Wavelet, the other technique used is Set Partitioning in Hierarchical Tree and the third technique used is Spatial-Orientation Tree Wavelet. Denoising is done using different wavelets Daubechies, Haar, Biorthogonal and Fejer-Korovkin. The impact of the wavelet compression and denoising techniques on recognition performance are compared with False Rejection Rate and False Acceptance Rate. The quality of a compressed image is calculated with different quality metrics. This work establishes that compression and denoising of the images minimally affect the recognition performance.

Iris recognition is one of the most reliable biometric technologies. The performance of an iris recognition system can be undermined by poor quality images and result in high false reject rates (FRR) and failure to enroll (FTE) rates. The selection of the features subset and the classification has become an important issue in the field of iris recognition. In this paper, a wavelet-based quality measure for iris images is proposed. The proposed method includes three modules: image preprocessing, feature extraction and recognition modules. The feature extraction module adopts the wavelet transform as the discriminating features. Similarity between two iris images is estimated using Euclidean distance measures. Features extracted using higher level wavelet decompositions are shown to yield better clustering and higher success rate in recognition.

Iris is claimed to be one of the best biometrics. We have collected a large data set of iris images, intentionally sampling a range of quality broader than that used by current commercial iris recognition systems. We have re-implemented the Daugman-like iris recognition algorithm developed by Masek. We have also developed and implemented an improved iris segmentation and eyelid detection stage of the algorithm, and experimentally verified the improvement in recognition performance using the collected dataset. Compared to Masek's original segmentation approach, our improved segmentation algorithm leads to an increase of over 6% in the rank-one recognition rate.

This report describes the large-scale experimental results from the Face Recognition Vendor Test (FRVT) 2006 and the Iris Challenge Evaluation (ICE) 2006. The FRVT 2006 looks at recognition from high-resolution still images and three-dimensional (3D) face images, and measures performance for still images taken under controlled and uncontrolled illumination. The ICE 2006 reports iris recognition performance from left and right iris images. The FRVT 2006 results from controlled still images and 3D images document an order-of-magnitude improvement in recognition performance over the FRVT 2002. This order-of-magnitude improvement was one of the goals of the preceding technology development effort, the Face Recognition Grand Challenge (FRGC). The FRVT 2006 and the ICE 2006 compared recognition performance from very-high resolution still face images, 3D face images, and single-iris images. On the FRVT 2006 and the ICE 2006 datasets, recognition performance was comparable for all three biometrics. In an experiment comparing human and algorithm performance, the best-performing face recognition algorithms were more accurate than humans. These and other results are discussed in detail.

Nowadays, researcher is focus in developing reliable iris recognition systems for non-cooperative situations. The demand for iris recognition is increasing due to its reliability, accuracy and uniqueness. There are major factors involved in unconstrained environment such as obstruction by eyelids, eyelashes, glass frames, hair, off-angle, presence of contact lenses, poor illumination, motion blur, lighting and specular reflections, partially eye image, etc. The performance of the iris will be deteriorated and this results in lower recognition rate. In this paper, an overview of iris recognition for noisy imaging environments is presented included various related databases for iris recognition systems.

The captured face images are of all kinds of quality in a face recognition system, some poor quality images, for example blurry images, could degrade the performance of the face recognition system. It is obliged to assess the quality of the acquired images in order to eliminate the bad effects result from poor quality images. The current techniques of image quality assessment are not suitable for the quality assessment of face images. This paper analyzes the characters of face recognition and then proposes a quantitative clarity assessment method in detail. The experimental results show that the proposed method can assess the quality of the face images accurately and quickly. The method manages to satisfy the real-time requirement and can be adopted as a preprocessing step in the automatic face recognition system conveniently.