Predictive models for multibiometric systems

Recognizing a subject given a set of biometrics is a fundamental pattern recognition problem. This paper builds novel statistical models for multibiometric systems using geometric and multinomial distributions. These models are generic as they are only based on the similarity scores produced by a recognition system. They predict the bounds on the range of indices within which a test subject is likely to be present in a sorted set of similarity scores. These bounds are then used in the multibiometric recognition system to predict a smaller subset of subjects from the database as probable candidates for a given test subject. Experimental results show that the proposed models enhance the recognition rate beyond the underlying matching algorithms for multiple face views, fingerprints, palm prints, irises and their combinations

Quality-based iris segmentation-level fusion

Iris localisation and segmentation are challenging and critical tasks in iris biometric recognition. Especially in non-cooperative and less ideal environments, their impact on overall system performance has been identified as a major issue. In order to avoid a propagation of system errors along the processing chain, this paper investigates iris fusion at segmentation-level prior to feature extraction and presents a framework for this task. A novel intelligent reference method for iris segmentation-level fusion is presented, which uses a learning-based approach predicting ground truth segmentation performance from quality indicators and model-based fusion to create combined boundaries. The new technique is analysed with regard to its capability to combine segmentation results (pupillary and limbic boundaries) of multiple segmentation algorithms. Results are validated on pairwise combinations of four open source iris segmentation algorithms with regard to the public CASIA and IITD iris databases illustrating the high versatility of the proposed method

Curved Gabor Filters for Fingerprint Image Enhancement

Gabor filters play an important role in many application areas for the enhancement of various types of images and the extraction of Gabor features. For the purpose of enhancing curved structures in noisy images, we introduce curved Gabor filters which locally adapt their shape to the direction of flow. These curved Gabor filters enable the choice of filter parameters which increase the smoothing power without creating artifacts in the enhanced image. In this paper, curved Gabor filters are applied to the curved ridge and valley structure of low-quality fingerprint images. First, we combine two orientation field estimation methods in order to obtain a more robust estimation for very noisy images. Next, curved regions are constructed by following the respective local orientation and they are used for estimating the local ridge frequency. Lastly, curved Gabor filters are defined based on curved regions and they are applied for the enhancement of low-quality fingerprint images. Experimental results on the FVC2004 databases show improvements of this approach in comparison to state-of-the-art enhancement methods

Learning Visual Classifiers From Limited Labeled Images

Recognizing humans and their activities from images and video is one of the key goals of computer vision. While supervised learning algorithms like Support Vector Machines and Boosting have offered robust solutions, they require large amount of labeled data for good performance. It is often difficult to acquire large labeled datasets due to the significant human effort involved in data annotation. However, it is considerably easier to collect unlabeled data due to the availability of inexpensive cameras and large public databases like Flickr and YouTube. In this dissertation, we develop efficient machine learning techniques for visual classification from small amount of labeled training data by utilizing the structure in the testing data, labeled data in a different domain and unlabeled data. This dissertation has three main parts. In the first part of the dissertation, we consider how multiple noisy samples available during testing can be utilized to perform accurate visual classification. Such multiple samples are easily available in video-based recognition problem, which is commonly encountered in visual surveillance. Specifically, we study the problem of unconstrained human recognition from iris images. We develop a Sparse Representation-based selection and recognition scheme, which learns the underlying structure of clean images. This learned structure is utilized to develop a quality measure, and a quality-based fusion scheme is proposed to combine the varying evidence. Furthermore, we extend the method to incorporate privacy, an important requirement inpractical biometric applications, without significantly affecting the recognition performance. In the second part, we analyze the problem of utilizing labeled data in a different domain to aid visual classification. We consider the problem of shifts in acquisition conditions during training and testing, which is very common in iris biometrics. In particular, we study the sensor mismatch problem, where the training samples are acquired using a sensor much older than the one used for testing. We provide one of the first solutions to this problem, a kernel learning framework to adapt iris data collected from one sensor to another. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to considerable improvement in cross sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems. In the last part of the dissertation, we analyze how unlabeled data available during training can assist visual classification applications. Here, we consider still image-based vision applications involving humans, where explicit motion cues are not available. A human pose often conveys not only the configuration of the body parts, but also implicit predictive information about the ensuing motion. We propose a probabilistic framework to infer this dynamic information associated with a human pose, using unlabeled and unsegmented videos available during training. The inference problem is posed as a non-parametric density estimation problem on non-Euclidean manifolds. Since direct modeling is intractable, we develop a data driven approach, estimating the density for the test sample under consideration. Statistical inference on the estimated density provides us with quantities of interest like the most probable future motion of the human and the amount of motion informatio

Popular Ensemble Methods: An Empirical Study

An ensemble consists of a set of individually trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods on 23 data sets using both neural networks and decision trees as our classification algorithm. Our results clearly indicate a number of conclusions. First, while Bagging is almost always more accurate than a single classifier, it is sometimes much less accurate than Boosting. On the other hand, Boosting can create ensembles that are less accurate than a single classifier -- especially when using neural networks. Analysis indicates that the performance of the Boosting methods is dependent on the characteristics of the data set being examined. In fact, further results show that Boosting ensembles may overfit noisy data sets, thus decreasing its performance. Finally, consistent with previous studies, our work suggests that most of the gain in an ensemble's performance comes in the first few classifiers combined; however, relatively large gains can be seen up to 25 classifiers when Boosting decision trees