Fayin Li | Search PubMed

PubMed2005

Open Set face recognition using transduction.

This paper motivates and describes a novel realization of transductive inference that can address the Open Set face recognition task. Open Set operates under the assumption that not all the test probes have mates in the gallery. It either detects the presence of some biometric signature within the gallery and finds its identity or rejects it, i.e., it provides for the "none of the above" answer. The main contribution of the paper is Open Set TCM-kNN (Transduction Confidence Machine-k Nearest Neighbors), which is suitable for multiclass authentication operational scenarios that have to include a rejection option for classes never enrolled in the gallery. Open Set TCM-kNN, driven by the relation between transduction and Kolmogorov complexity, provides a local estimation of the likelihood ratio needed for detection tasks. We provide extensive experimental data to show the feasibility, robustness, and comparative advantages of Open Set TCM-kNN on Open Set identification and watch list (surveillance) tasks using challenging FERET data. Last, we analyze the error structure driven by the fact that most of the errors in identification are due to a relatively small number of face patterns. Open Set TCM-kNN is shown to be suitable for PSEI (pattern specific error inhomogeneities) error analysis in order to identify difficult to recognize faces. PSEI analysis improves biometric performance by removing a small number of those difficult to recognize faces responsible for much of the original error in performance and/or by using data fusion.

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PubMed2004

Motion estimation using Statistical Learning Theory.

This paper describes a novel application of Statistical Learning Theory (SLT) to single motion estimation and tracking. The problem of motion estimation can be related to statistical model selection, where the goal is to select one (correct) motion model from several possible motion models, given finite noisy samples. SLT, also known as Vapnik-Chervonenkis (VC), theory provides analytic generalization bounds for model selection, which have been used successfully for practical model selection. This paper describes a successful application of an SLT-based model selection approach to the challenging problem of estimating optimal motion models from small data sets of image measurements (flow). We present results of experiments on both synthetic and real image sequences for motion interpolation and extrapolation; these results demonstrate the feasibility and strength of our approach. Our experimental results show that for motion estimation applications, SLT-based model selection compares favorably against alternative model selection methods, such as the Akaike's fpe, Schwartz' criterion (sc), Generalized Cross-Validation (gcv), and Shibata's Model Selector (sms). The paper also shows how to address the aperture problem using SLT-based model selection for penalized linear (ridge regression) formulation.

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