Robust Recognition-by-Parts Using Transduction and Boosting with Applications to Biometrics

Summary form only given. The ability to recognize objects, in general, and living creatures, in particular, in photographs or video clips, is a critical enabling technology for a wide range of applications including health care, human-computer intelligent interaction, search engines for image retrieval and data mining, industrial and personal robotics, surveillance and security, and transportation. Despite almost 50 years of research, however, today's object recognition systems are still largely unable to handle the extraordinary wide range of appearances assumed by common objects [including human faces] in typical images. Some of the challenges for modern pattern recognition that have to be addressed in order to advance and make practical both detection and categorization include open set recognition, occlusion and masking, change detection and time-varying imagery, lack of enough data for training, and proper performance evaluation and error analysis. Open set recognition operates under the assumption that not all the test (unknown) probes have mates in the gallery (training set), occlusion and masking hide and disguise parts of the input, image contents vary across both the spatial and temporal dimensions, the amount of data available for learning and adaptation is limited, and errors are not uniformly distributed across patterns. The recognition-by-parts approach proposed here to address the challenges listed above is driven by transduction and boosting. Transduction employs local estimation and inference to find a compatible labeling of joined training and test data. Active learning further promotes the recognition process by making incremental choices about what is best to learn and when in order to accumulate the evidence needed to disambiguate among alternative interpretations. The interplay between labeled ("training") and unlabeled ("test"') data points mediates between semi-supervised learning and transduction. The additional information coming from the unlabeled data points includes consn-aints and hints about the meaningful relations and regularities affecting their very discrimination. Boosting combines in an iterative fashion part-based, model-free, and non-parametric simple weak classifiers, whose contents and relative ranking are driven by their "strangeness" characteristics. The scope of the proposed approach covers also stream-based data points and includes change detection. The benefits of the proposed discriminative recognition-by-parts approach include a priori setting of rejection thresholds, no need for image segmentation, robustness to occlusion, clutter, and disguise. Examples drawn from biometrics illustrate the proposed approach and show its feasibility and utility.