Issue No. 11 - November (2005 vol. 27)
Harry Wechsler , IEEE
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.
Index Terms- Biometrics, confidence, credibility, data fusion, information quality, Kolmogorov complexity, face recognition, open set recognition, performance evaluation, PSEI (pattern specific error inhomogeneities), randomness deficiency, strangeness, face surveillance, (multiclass) transduction, watch list, clustering, outlier detection.
H. Wechsler and F. Li, "Open Set Face Recognition Using Transduction," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 27, no. , pp. 1686-1697, 2005.