The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Sparse Methods for Robust and Efficient Visual Recognition

Visual recognition has been a subject of extensive research in computer vision. A vast literature exists on feature extraction and learning methods for recognition. However, due to large variations in visual data, robust visual recognition is still an open problem. In recent years, sparse representation-based methods have become popular for visual recognition. By learning a compact dictionary of data and exploiting the notion of sparsity, start-of-the-art results have been obtained on many recognition tasks. However, existing data-driven sparse model techniques may not be optimal for some challenging recognition problems. In this dissertation, we consider some of these recognition tasks and present approaches based on sparse coding for robust and efficient recognition in such cases. First we study the problem of low-resolution face recognition. This is a challenging problem, and methods have been proposed using super-resolution and machine learning based techniques. However, these methods cannot handle variations like illumination changes which can happen at low resolutions, and degrade the performance. We propose a generative approach for classifying low resolution faces, by exploiting 3D face models. Further, we propose a joint sparse coding framework for robust classification at low resolutions. The effectiveness of the method is demonstrated on different face datasets. In the second part, we study a robust feature-level fusion method for multimodal biometric recognition. Although score-level and decision-level fusion methods exist in biometric literature, feature-level fusion is challenging due to different output formats of biometric modalities. In this work, we propose a novel sparse representation-based method for multimodal fusion, and present experimental results for a large multimodal dataset. Robustness to noise and occlusion are demonstrated. In the third part, we consider the problem of domain adaptation, where we want to learn effective classifiers for cases where the test images come from a different distribution than the training data. Typically, due to high cost of human annotation, very few labeled samples are available for images in the test domain. Specifically, we study the problem of adapting sparse dictionary-based classification methods for such cases. We describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both domains in the projected low dimensional space. The proposed method is efficient and performs on par or better than many competing state-of-the-art methods. Lastly, we study an emerging analysis framework of sparse coding for image classification. We show that the analysis sparse coding can give similar performance as the typical synthesis sparse coding methods, while being much faster at sparse encoding. In the end, we conclude the dissertation with discussions and possible future directions

Multimodal Learning and Its Application to Mobile Active Authentication

Mobile devices are becoming increasingly popular due to their flexibility and convenience in managing personal information such as bank accounts, profiles and passwords. With the increasing use of mobile devices comes the issue of security as the loss of a smartphone would compromise the personal information of the user. Traditional methods for authenticating users on mobile devices are based on passwords or fingerprints. As long as mobile devices remain active, they do not incorporate any mechanisms for verifying if the user originally authenticated is still the user in control of the mobile device. Thus, unauthorized individuals may improperly obtain access to personal information of the user if a password is compromised or if a user does not exercise adequate vigilance after initial authentication on a device. To deal with this problem, active authentication systems have been proposed in which users are continuously monitored after the initial access to the mobile device. Active authentication systems can capture users' data (facial image data, screen touch data, motion data, etc) through sensors (camera, touch screen, accelerometer, etc), extract features from different sensors' data, build classification models and authenticate users via comparing additional sensor data against the models. Mobile active authentication can be viewed as one application of the more general problem, namely, multimodal classification. The idea of multimodal classification is to utilize multiple sources (modalities) measuring the same instance to improve the overall performance compared to using a single source (modality). Multimodal classification also arises in many computer vision tasks such as image classification, RGBD object classification and scene recognition. In this dissertation, we not only present methods and algorithms related to active authentication problems, but also propose multimodal recognition algorithms based on low-rank and joint sparse representations as well as multimodal metric learning algorithm to improve multimodal classification performance. The multimodal learning algorithms proposed in this dissertation make no assumption about the feature type or applications, thus they can be applied to various recognition tasks such as mobile active authentication, image classification and RGBD recognition. First, we study the mobile active authentication problem by exploiting a dataset consisting of 50 users' face captured by the phone's frontal camera and screen touch data sensed by the screen for evaluating active authentication algorithms developed under this research. The dataset is named as UMD Active Authentication (UMDAA) dataset. Details on data preprocessing and feature extraction for touch data and face data are described respectively. Second, we present an approach for active user authentication using screen touch gestures by building linear and kernelized dictionaries based on sparse representations and associated classifiers. Experiments using the screen touch data components of UMDAA dataset as well as two other publicly available screen touch datasets show that the dictionary-based classification method compares favorably to those discussed in the literature. Experiments done using screen touch data collected in three different sessions show a drop in performance when the training and test data come from different sessions. This suggests a need for applying domain adaptation methods to further improve the performance of the classifiers. Third, we propose a domain adaptive sparse representation-based classification method that learns projections of data in a space where the sparsity of data is maintained. We provide an efficient iterative procedure for solving the proposed optimization problem. One of the key features of the proposed method is that it is computationally efficient as learning is done in the lower-dimensional space. Various experiments on UMDAA dataset show that our method is able to capture the meaningful structure of data and can perform significantly better than many competitive domain adaptation algorithms. Fourth, we propose low-rank and joint sparse representations-based multimodal recognition. Our formulations can be viewed as generalized versions of multivariate low-rank and sparse regression, where sparse and low-rank representations across all the modalities are imposed. One of our methods takes into account coupling information within different modalities simultaneously by enforcing the common low-rank and joint sparse representation among each modality's observations. We also modify our formulations by including an occlusion term that is assumed to be sparse. The alternating direction method of multipliers is proposed to efficiently solve the proposed optimization problems. Extensive experiments on UMDAA dataset, WVU multimodal biometrics dataset and Pascal-Sentence image classification dataset show that that our methods provide better recognition performance than other feature-level fusion methods. Finally, we propose a hierarchical multimodal metric learning algorithm for multimodal data in order to improve multimodal classification performance. We design metric for each modality as a product of two matrices: one matrix is modality specific, the other is enforced to be shared by all the modalities. The modality specific projection matrices capture the varying characteristics exhibited by multiple modalities and the common projection matrix establishes the relationship of the distance metrics corresponding to multiple modalities. The learned metrics significantly improves classification accuracy and experimental results of tagged image classification problem as well as various RGBD recognition problems show that the proposed algorithm outperforms existing learning algorithms based on multiple metrics as well as other state-of-the-art approaches tested on these datasets. Furthermore, we make the proposed multimodal metric learning algorithm non-linear by using kernel methods

Eye Tracking Methods for Analysis of Visuo-Cognitive Behavior in Medical Imaging

Predictive modeling of human visual search behavior and the underlying metacognitive processes is now possible thanks to significant advances in bio-sensing device technology and machine intelligence. Eye tracking bio-sensors, for example, can measure psycho-physiological response through change events in configuration of the human eye. These events include positional changes such as visual fixation, saccadic movements, and scanpath, and non-positional changes such as blinks and pupil dilation and constriction. Using data from eye-tracking sensors, we can model human perception, cognitive processes, and responses to external stimuli. In this study, we investigated the visuo-cognitive behavior of clinicians during the diagnostic decision process for breast cancer screening under clinically equivalent experimental conditions involving multiple monitors and breast projection views. Using a head-mounted eye tracking device and a customized user interface, we recorded eye change events and diagnostic decisions from 10 clinicians (three breast-imaging radiologists and seven Radiology residents) for a corpus of 100 screening mammograms (comprising cases of varied pathology and breast parenchyma density). We proposed novel features and gaze analysis techniques, which help to encode discriminative pattern changes in positional and non-positional measures of eye events. These changes were shown to correlate with individual image readers' identity and experience level, mammographic case pathology and breast parenchyma density, and diagnostic decision. Furthermore, our results suggest that a combination of machine intelligence and bio-sensing modalities can provide adequate predictive capability for the characterization of a mammographic case and image readers diagnostic performance. Lastly, features characterizing eye movements can be utilized for biometric identification purposes. These findings are impactful in real-time performance monitoring and personalized intelligent training and evaluation systems in screening mammography. Further, the developed algorithms are applicable in other application domains involving high-risk visual tasks

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity