Socio-Emotional Functioning and Face Recognition Ability in the Normal Population

Recent research indicates face recognition ability varies within the normal population. To date, two factors have been identified that influence this cognitive process: the age and gender of the perceiver. In this paper, we examine the influence of socio-emotional functioning on face recognition ability. We invited participants with high and low levels of empathy (as indicated by the Empathy Quotient) to take part in a face recognition test. Participants were asked to study a set of faces, and at test viewed the studied faces intermixed with novel faces. As predicted, high empaths achieved higher scores in the face recognition test compared to low empaths. This pattern of findings provides further evidence that face recognition ability varies within the normal population, and suggests socio-emotional functioning may be an additional factor that influences face recognition ability

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

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

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

Incremental Art: A Neural Network System for Recognition by Incremental Feature Extraction

Abstract Incremental ART extends adaptive resonance theory (ART) by incorporating mechanisms for efficient recognition through incremental feature extraction. The system achieves efficient confident prediction through the controlled acquisition of only those features necessary to discriminate an input pattern. These capabilities are achieved through three modifications to the fuzzy ART system: (1) A partial feature vector complement coding rule extends fuzzy ART logic to allow recognition based on partial feature vectors. (2) The addition of a F2 decision criterion to measure ART predictive confidence. (3) An incremental feature extraction layer computes the next feature to extract based on a measure of predictive value. Our system is demonstrated on a face recognition problem but has general applicability as a machine vision solution and as model for studying scanning patterns.Office of Naval Research (N00014-92-J-4015, N00014-92-J-1309, N00014-91-4100); Air Force Office of Scientific Research (90-0083); National Science Foundation (IRI 90-00530