Automatic vehicle tracking and recognition from aerial image sequences

This paper addresses the problem of automated vehicle tracking and recognition from aerial image sequences. Motivated by its successes in the existing literature focus on the use of linear appearance subspaces to describe multi-view object appearance and highlight the challenges involved in their application as a part of a practical system. A working solution which includes steps for data extraction and normalization is described. In experiments on real-world data the proposed methodology achieved promising results with a high correct recognition rate and few, meaningful errors (type II errors whereby genuinely similar targets are sometimes being confused with one another). Directions for future research and possible improvements of the proposed method are discussed

Face Identification and Clustering

In this thesis, we study two problems based on clustering algorithms. In the first problem, we study the role of visual attributes using an agglomerative clustering algorithm to whittle down the search area where the number of classes is high to improve the performance of clustering. We observe that as we add more attributes, the clustering performance increases overall. In the second problem, we study the role of clustering in aggregating templates in a 1:N open set protocol using multi-shot video as a probe. We observe that by increasing the number of clusters, the performance increases with respect to the baseline and reaches a peak, after which increasing the number of clusters causes the performance to degrade. Experiments are conducted using recently introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition datasets

Gradient edge map features for frontal face recognition under extreme illumination changes

Our aim in this paper is to robustly match frontal faces in the presence of extreme illumination changes, using only a single training image per person and a single probe image. In the illumination conditions we consider, which include those with the dominant light source placed behind and to the side of the user, directly above and pointing downwards or indeed below and pointing upwards, this is a most challenging problem. The presence of sharp cast shadows, large poorly illuminated regions of the face, quantum and quantization noise and other nuisance effects, makes it difficult to extract a sufficiently discriminative yet robust representation. We introduce a representation which is based on image gradient directions near robust edges which correspond to characteristic facial features. Robust edges are extracted using a cascade of processing steps, each of which seeks to harness further discriminative information or normalize for a particular source of extra-personal appearance variability. The proposed representation was evaluated on the extremely difficult YaleB data set. Unlike most of the previous work we include all available illuminations, perform training using a single image per person and match these also to a single probe image. In this challenging evaluation setup, the proposed gradient edge map achieved 0.8% error rate, demonstrating a nearly perfect receiver-operator characteristic curve behaviour. This is by far the best performance achieved in this setup reported in the literature, the best performing methods previously proposed attaining error rates of approximately 6–7%

Biometric Authentication System on Mobile Personal Devices

We propose a secure, robust, and low-cost biometric authentication system on the mobile personal device for the personal network. The system consists of the following five key modules: 1) face detection; 2) face registration; 3) illumination normalization; 4) face verification; and 5) information fusion. For the complicated face authentication task on the devices with limited resources, the emphasis is largely on the reliability and applicability of the system. Both theoretical and practical considerations are taken. The final system is able to achieve an equal error rate of 2% under challenging testing protocols. The low hardware and software cost makes the system well adaptable to a large range of security applications

Social interactions, emotion and sleep: a systematic review and research agenda

Sleep and emotion are closely linked, however the effects of sleep on socio-emotional task performance have only recently been investigated. Sleep loss and insomnia have been found to affect emotional reactivity and social functioning, although results, taken together, are somewhat contradictory. Here we review this advancing literature, aiming to 1) systematically review the relevant literature on sleep and socio-emotional functioning, with reference to the extant literature on emotion and social interactions, 2) summarize results and outline ways in which emotion, social interactions, and sleep may interact, and 3) suggest key limitations and future directions for this field. From the reviewed literature, sleep deprivation is associated with diminished emotional expressivity and impaired emotion recognition, and this has particular relevance for social interactions. Sleep deprivation also increases emotional reactivity; results which are most apparent with neuro-imaging studies investigating amygdala activity and its prefrontal regulation. Evidence of emotional dysregulation in insomnia and poor sleep has also been reported. In general, limitations of this literature include how performance measures are linked to self-reports, and how results are linked to socio-emotional functioning. We conclude by suggesting some possible future directions for this field

Self-affine Manifolds

This paper studies closed 3-manifolds which are the attractors of a system of finitely many affine contractions that tile $\mathbb{R}^3$. Such attractors are called self-affine tiles. Effective characterization and recognition theorems for these 3-manifolds as well as theoretical generalizations of these results to higher dimensions are established. The methods developed build a bridge linking geometric topology with iterated function systems and their attractors. A method to model self-affine tiles by simple iterative systems is developed in order to study their topology. The model is functorial in the sense that there is an easily computable map that induces isomorphisms between the natural subdivisions of the attractor of the model and the self-affine tile. It has many beneficial qualities including ease of computation allowing one to determine topological properties of the attractor of the model such as connectedness and whether it is a manifold. The induced map between the attractor of the model and the self-affine tile is a quotient map and can be checked in certain cases to be monotone or cell-like. Deep theorems from geometric topology are applied to characterize and develop algorithms to recognize when a self-affine tile is a topological or generalized manifold in all dimensions. These new tools are used to check that several self-affine tiles in the literature are 3-balls. An example of a wild 3-dimensional self-affine tile is given whose boundary is a topological 2-sphere but which is not itself a 3-ball. The paper describes how any 3-dimensional handlebody can be given the structure of a self-affine 3-manifold. It is conjectured that every self-affine tile which is a manifold is a handlebody.Comment: 40 pages, 13 figures, 2 table

UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

Humor is a unique and creative communicative behavior displayed during social interactions. It is produced in a multimodal manner, through the usage of words (text), gestures (vision) and prosodic cues (acoustic). Understanding humor from these three modalities falls within boundaries of multimodal language; a recent research trend in natural language processing that models natural language as it happens in face-to-face communication. Although humor detection is an established research area in NLP, in a multimodal context it is an understudied area. This paper presents a diverse multimodal dataset, called UR-FUNNY, to open the door to understanding multimodal language used in expressing humor. The dataset and accompanying studies, present a framework in multimodal humor detection for the natural language processing community. UR-FUNNY is publicly available for research