Fayin Li

Micro-Doppler effect in radar: phenomenon, model, and simulation study

When, in addition to the constant Doppler frequency shift induced by the bulk motion of a radar target, the target or any structure on the target undergoes micro-motion dynamics, such as mechanical vibrations or rotations, the micro-motion dynamics induce Doppler modulations on the returned signal, referred to as the micro-Doppler effect. We introduce the micro-Doppler phenomenon in radar, develop a model of Doppler modulations, derive formulas of micro-Doppler induced by targets with vibration, rotation, tumbling and coning motions, and verify them by simulation studies, analyze time-varying micro-Doppler features using high-resolution time-frequency transforms, and demonstrate the micro-Doppler effect observed in real radar data.

Integrating perceptual and cognitive modeling for adaptive and intelligent human-computer interaction

This paper describes technology and tools for intelligent human-computer interaction (IHCI) in which human cognitive, perceptual, motor and affective factors are modeled and used to adapt the H-C interface. IHCI emphasizes that human behavior encompasses both apparent human behavior and the hidden mental state behind behavioral performance. IHCI expands on the interpretation of human activities, known as W4 (what, where, when, who). While W4 only addresses the apparent perceptual aspect of human behavior the W5+ technology for IHCI described in this paper addresses also the why and how questions, whose solution requires recognizing specific cognitive states. IHCI integrates parsing and interpretation of nonverbal information with a computational cognitive model of the user which, in turn, feeds into processes that adapt the interface to enhance operator performance and provide for rational decision-making. The technology proposed is based on a general four-stage interactive framework, which moves from parsing the raw sensory-motor input, to interpreting the users motions and emotions, to building an understanding of the users current cognitive state. It then diagnoses various problems in the situation and adapts the interface appropriately. The interactive component of the system improves processing at each stage. Examples of perceptual, behavioral, and cognitive tools are described throughout the paper Adaptive and intelligent HCI are important for novel applications of computing, including ubiquitous and human-centered computing.

Vision based topological Markov localization

In this paper we study the problem of acquiring a topological model of indoors environment by means of visual sensing and subsequent localization given the model. The resulting model consists of a set of locations and neighborhood relationships between them. Each location in the model is represented by a collection of representative views and their associated descriptors selected from a temporally sub-sampled video stream captured by a mobile robot during exploration. We compare the recognition performance using global image histograms as well as local scale-invariant features as image descriptors, demonstrate their strengths and weaknesses and show how to model the spatial relationships between individual locations by a Hidden Markov Model. The quality of the acquired model is tested in the localization stage by means of location recognition: given a new view or a sequence of views, the most likely location where that view came from is determined.

Global localization and relative positioning based on scale-invariant keypoints

Abstract The localization capability of a mobile robot is central to basic navigation and map building tasks. We describe a probabilistic environment model which facilitates global localization scheme by means of location recognition. In the exploration stage the environment is partitioned into locations, each characterized by a set of scale-invariant keypoints. The descriptors associated with these keypoints can be robustly matched despite changes in contrast, scale and viewpoint. We demonstrate the efficacy of these features for location recognition, where given a new view the most likely location from which this view came from is determined. The misclassifications due to dynamic changes in the environment or inherent appearance ambiguities are overcome by exploiting location neighborhood relationships captured by a Hidden Markov Model. We report the recognition performance of this approach in an indoor environment consisting of eighteen locations and discuss the suitability of this approach for a more general class of recognition problems. Once the most likely location has been determined we demonstrate how to robustly compute the relative pose between the representative view and the current view.

Face authentication using recognition-by-parts, boosting, and transduction

A robust recognition-by-parts authentication system for comparing and authenticating a test image with at least one training image is disclosed. This invention applies the concepts of recognition-by-parts, boosting, and transduction.

Probabilistic location recognition using reduced feature set

The localization capability is central to basic navigation tasks and motivates development of various visual navigation systems. In this paper we describe a two stage approach for localization in indoor environments. In the first stage, the environment is partitioned into several locations, each characterized by a set of scale-invariant keypoints and their associated descriptors. In the second stage the keypoints of the query view are integrated probabilistically yielding an estimate of most likely location. The novelty of our approach is in the selection of discriminative features, best suited for characterizing individual locations. We demonstrate that high location recognition rate is maintained with only 10% of the originally detected features, yielding a substantial speedup in recognition and capability of handling larger environments. The ambiguities due to the self-similarity and dynamic changes in the environment are resolved by exploiting spatial relationships between locations captured by hidden Markov model

Strangeness Based Feature Selection for Part Based Recognition

Motivated by recent approaches to object recognition, where objects are represented in terms of parts, we propose a new algorithm for selecting discriminative features based on strangeness measure. We will show that k-nearest neighbour strangeness can be used to measure the uncertainty of individual features with respect to the class labels and forms piecewise constant decision boundary. We study its properties and generalization capability by comparing it with optimal decision boundary and boundary obtained by k-nearest-neighbor methods. The proposed feature selection algorithm is tested both in simulation and real experiments, demonstrating that meaningful discriminative local features are selected despite the presence of large numbers of distractors. In the second stage we demonstrate how to integrate the local evidence provided by the selected features in the boosting framework in order to obtain the final strong classifier. The performance of the feature selection algorithm and the classifier is evaluated on the Caltech five object category database, achieving superior results in comparison with existing approaches at lower computational cost.

Using normal flow for detection and tracking of limbs in color images

Humans are articulated objects composed of non-rigid parts. We are interested in detecting and tracking human motions over various periods of time. We describe a method of detecting and tracking human body parts in color video sequences. The dominant motion region is detected using normal flow; expectation maximization, uniform sampling, and a shortest path algorithm are used to find the bounding contour for the moving arm. An affine motion model is fit to the arm region; residual analysis and outlier rejection are used for robust parameter estimation. The estimated parameters are used for the prediction of the location of the moving limb in the next frame. Detection and tracking results are combined to account for the deviations from the affine flow model and increase the robustness of the method. We demonstrate our method on several long image sequences corresponding to different limb movements.

Watch List Face Surveillance Using Transductive Inference

The open set recognition task, most challenging among the biometric tasks, operates under the assumption that not all the probes have mates in the gallery. It requires the availability of a reject option. For face recognition open set corresponds to the watch listface surveillance task, where the face recognition engine must detect or reject the probe. The above challenges are addressed successfully in this paper using transduction, which is a novel form of inductive learning. Towards that end, we introduce the Open SetTCM-kNN algorithm, which is based upon algorithmic randomness and transductive inference. It is successfully validated on the (small) watch list task (80% or more of the probes lack mates) using FERET datasets. In particular, Open Set TCM-kNN provides on the average 96% correct detection / rejection and identification using the PCA and/or Fisherfaces components for face representation.

Robust fusion using boosting and transduction for component-based face recognition

Face recognition performance depends upon the input variability as encountered during biometric data capture including occlusion and disguise. The challenge met in this paper is to expand the scope and utility of biometrics by discarding unwarranted assumptions regarding the completeness and quality of the data captured. Towards that end we propose a model-free and non-parametric component-based face recognition strategy with robust decisions for data fusion that are driven by transduction and boosting. The conceptual framework draws support throughout from discriminative methods using likelihood ratios. It links at the conceptual level forensics and biometrics, while at the implementation level it links the Bayesian framework and statistical learning theory (SLT). Feature selection of local patch instances and their corresponding high-order combinations, exemplar-based clustering (of patches) as components including the sharing (of exemplars) among components, and finally decision-making regarding authentication using boosting driven by components that play the role of weak-learners, are implemented in a similar fashion using transduction driven by a strangeness measure akin to typicality. The feasibility, reliability, and utility of the proposed open set face recognition architecture vis-a-vis adverse image capture conditions are illustrated using FRGC data. The potential for future developments concludes the paper.