Data-Driven Segmentation of Post-mortem Iris Images

This paper presents a method for segmenting iris images obtained from the deceased subjects, by training a deep convolutional neural network (DCNN) designed for the purpose of semantic segmentation. Post-mortem iris recognition has recently emerged as an alternative, or additional, method useful in forensic analysis. At the same time it poses many new challenges from the technological standpoint, one of them being the image segmentation stage, which has proven difficult to be reliably executed by conventional iris recognition methods. Our approach is based on the SegNet architecture, fine-tuned with 1,300 manually segmented post-mortem iris images taken from the Warsaw-BioBase-Post-Mortem-Iris v1.0 database. The experiments presented in this paper show that this data-driven solution is able to learn specific deformations present in post-mortem samples, which are missing from alive irises, and offers a considerable improvement over the state-of-the-art, conventional segmentation algorithm (OSIRIS): the Intersection over Union (IoU) metric was improved from 73.6% (for OSIRIS) to 83% (for DCNN-based presented in this paper) averaged over subject-disjoint, multiple splits of the data into train and test subsets. This paper offers the first known to us method of automatic processing of post-mortem iris images. We offer source codes with the trained DCNN that perform end-to-end segmentation of post-mortem iris images, as described in this paper. Also, we offer binary masks corresponding to manual segmentation of samples from Warsaw-BioBase-Post-Mortem-Iris v1.0 database to facilitate development of alternative methods for post-mortem iris segmentation

Learning Visual Classifiers From Limited Labeled Images

Recognizing humans and their activities from images and video is one of the key goals of computer vision. While supervised learning algorithms like Support Vector Machines and Boosting have offered robust solutions, they require large amount of labeled data for good performance. It is often difficult to acquire large labeled datasets due to the significant human effort involved in data annotation. However, it is considerably easier to collect unlabeled data due to the availability of inexpensive cameras and large public databases like Flickr and YouTube. In this dissertation, we develop efficient machine learning techniques for visual classification from small amount of labeled training data by utilizing the structure in the testing data, labeled data in a different domain and unlabeled data. This dissertation has three main parts. In the first part of the dissertation, we consider how multiple noisy samples available during testing can be utilized to perform accurate visual classification. Such multiple samples are easily available in video-based recognition problem, which is commonly encountered in visual surveillance. Specifically, we study the problem of unconstrained human recognition from iris images. We develop a Sparse Representation-based selection and recognition scheme, which learns the underlying structure of clean images. This learned structure is utilized to develop a quality measure, and a quality-based fusion scheme is proposed to combine the varying evidence. Furthermore, we extend the method to incorporate privacy, an important requirement inpractical biometric applications, without significantly affecting the recognition performance. In the second part, we analyze the problem of utilizing labeled data in a different domain to aid visual classification. We consider the problem of shifts in acquisition conditions during training and testing, which is very common in iris biometrics. In particular, we study the sensor mismatch problem, where the training samples are acquired using a sensor much older than the one used for testing. We provide one of the first solutions to this problem, a kernel learning framework to adapt iris data collected from one sensor to another. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to considerable improvement in cross sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems. In the last part of the dissertation, we analyze how unlabeled data available during training can assist visual classification applications. Here, we consider still image-based vision applications involving humans, where explicit motion cues are not available. A human pose often conveys not only the configuration of the body parts, but also implicit predictive information about the ensuing motion. We propose a probabilistic framework to infer this dynamic information associated with a human pose, using unlabeled and unsegmented videos available during training. The inference problem is posed as a non-parametric density estimation problem on non-Euclidean manifolds. Since direct modeling is intractable, we develop a data driven approach, estimating the density for the test sample under consideration. Statistical inference on the estimated density provides us with quantities of interest like the most probable future motion of the human and the amount of motion informatio

Feature-domain super-resolution framework for Gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe