Abstract

Representation of fingerprints is one of the key factors that limits the accuracy and efficiency of matching algorithms. Most popular methods represent each fingerprint as an unordered set of minutiae with variable cardinality and the matching algorithms are left with the task of finding the best correspondence between the two sets of minutiae. While this makes the representation more flexible and matching more accurate, the task becomes computationally intensive. Fixed length representations with aligned features are highly efficient to match. However, creating an aligned representation without the knowledge of the sample to which it is to be matched, makes the problem of representation more complex. Some of the fixed-length representations only provide partial alignment, leaving the rest to the matching stage. In this paper, we propose a fixed length representation for fingerprints that provides exact alignment between the features, thus enabling high-speed matching with minimal computational effort. The representation extends the idea of object representation using bag of words into a bag of minutiae neighborhoods. The representation is provably invariant to affine transformations (rotation, translation and uniform scaling), and is shown to be highly discriminative for the task of verification. Experimental results on FVC 2002 and 2004 datasets clearly show the superiority of the representation with competing methods. As the proposed representation can be computed from the standard minutiae templates, the method is applicable to existing datasets, where the original fingerprint images are not available.

Abstract

The ability to detect floor regions from an image enables a variety of applications such as indoor scene understanding, mobility assessment, robot navigation, path planning and surveillance. In this work, we propose a framework for estimating floor regions in cluttered indoor environments. The problem of floor detection and segmentation is challenging in situations where floor and non-floor regions have similar appearances. It is even harder to segment floor regions when clutter, specular reflections, shadows and textured floors are present within the scene. Our framework utilizes a generic classifier trained from appearance cues as well as floor density estimates, both trained from a variety of indoor images. The results of the classifier is then adapted to a specific test image where we integrate appearance, position and geometric cues in an iterative framework. A Markov Random Field framework is used to integrate the cues to segment floor regions. In contrast to previous settings that relied on optical flow, depth sensors or multiple images in a calibrated setup, our method can work on a single image. It is also more flexible as we avoid assumptions like Manhattan world scene or restricting clutter only to wall-floor boundaries. Experimental results on the public MIT Scene dataset as well as a more challenging dataset that we acquired, demonstrate the robustness and efficiency of our framework on the above mentioned complex situations.

Abstract

We consider the problem of automatic identification of faces in videos such as movies, given a dictionary of known faces from a public or an alternate database. This has applications in video indexing, content based search, surveillance, and real time recognition on wearable computers. We propose a two stage approach for this problem. First, we recognize the faces in a video using a sparse representation framework using l1-minimization and select a few key-frames based on a robust confidence measure.We then use transductive learning to propagate the labels from the key-frames to the remaining frames by incorporating constraints simultaneously in temporal and feature spaces. This is in contrast to some of the previous approaches where every test frame/track is identified independently, ignoring the correlation between the faces in video tracks. Having a few key frames belonging to few subjects for label propagation rather than a large dictionary of actors reduces the amount of confusion. We evaluate the performance of our algorithm on Movie Trailer face dataset and five movie clips, and achieve a significant improvement in labeling accuracy compared to previous approaches

Abstract

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Abstract

Widely used online commerce systems require an user to submit his sole banking credentials or credit card details for availing desired services, thus involving high risks with untrusted service providers. Often used one-time password based systems provide additional transaction security, but are still incapable of differentiating between a genuine user trying to authenticate or an adversary with stolen credentials. This brings out a strong need for biometrics based onetime password systems. In this paper we propose a one-time biometric token based authentication protocol which works within the framework of current online transaction schemes allowing an user to carry out a financial transaction with a service provider which completes with an authorization from the bank. The proposed protocol is based on key-binding biometric cryptosystems and upholds the requirements of secure authentication, template protection and revocability while providing privacy to individual’s biometrics and anonymity from the service provider. We demonstrate our system’s security and performance using iris biometrics to authenticate individuals.

Abstract

Motion trajectories extracted from certain videos contain sufficient spatio-temporal information which can be effectively used to characterize those videos. But the task of framing text-based queries for such videos in content-based video retrieval systems is very complicated. Sketch based query is an efficient tool to construct motion-based queries but perceptual differences like spatial and temporal variability pose serious challenges to query modelling. In this work we propose a new method of modelling sketch based queries which attempts to extract the qualitative features of motion by minimizing the perceptual variability. We also develop a multilevel filter for indexing a query, in which the search results are refined at each stage using a cumulative scoring mechanism. Finally, we show the effectiveness of our algorithm on a dataset of real pool videos and a synthetic dataset containing simulated videos having very complex motion trajectories.

Abstract

We present an approach for learning low- and high-level fingerprint structures in an unsupervised manner, which we use for enhancement of fingerprint images and estimation of orientation fields, frequency images, and region masks. We incorporate the use of a convolutional deep belief network to learn features from greyscale, clean fingerprint images. We also show that reconstruction performed by the learnt network works as a suitable enhancement of the fingerprint, and hierarchical probabilistic inference is able to estimate overall fingerprint structures as well. Our approach performs better than Gabor-based enhancement and short time Fourier transform-assisted enhancement on images it was trained on. We further use information from the learnt features in first layer, which are short and oriented ridge structures, to extract the orientation field, frequency image, and region mask of input fingerprints

Abstract

Concerns of privacy, template security and efficiency ofbiometric authentication systems have received consider-able research attention in the recent past. Binary templaterepresentations have been shown to provide significant im-provements in efficiency without compromising accuracyfor a variety of biometric modalities including fingerprints,palmprints and iris. Most of the secure authentication pro-tocols work with generic feature representations or proposenew secure templates for specific modalities. In this work,we propose an authentication protocol that works with anybinary biometric representation that employs hamming dis-tance for comparison and convert it into a secure, privacypreserving protocol that provides template protection. Wealso provide the ability to use masks while computing thehamming distance. The protocol retains the accuracy of theunderlying representation as demonstrated by our experi-ments

Abstract

Sparse representation based image restoration tech-niques have shown to be successful in solving various inverse problems such as denoising, in painting, and super-resolution,etc. on natural images and videos. In this paper, we explore the use of sparse representation based methods specifically to restore the degraded document images. While natural images form a very small subset of all possible images admitting the possibility of sparse representation, document images are significantly more restricted and are expected to be ideally suited for such a representation. However, the binary nature of textual document images makes dictionary learning and coding techniques unsuitable to be applied directly. We leverage the fact that different characters possess similar strokes, curves, and edges, and learn a dictionary that gives sparse decomposition for patches. Experimental results show significant improvement in image quality and OCR performance on documents collected from a variety of sources such as magazines and books. This method is therefore, ideally suited for restoring highly degraded images in repositories such as digital libraries.

Abstract

Robust segmentation of ballistic strokes fromonline handwritten traces is critical in parameter estimationof stroke based models for applications such as recognition,synthesis, and writer identification. In this paper we propose anew method for segmenting ballistic strokes from online hand-writing. Traditional methods of ballistic stroke segmentationrely on detection of local minima of pen speed. Unfortunately,this approach is highly sensitive to noise, in sensing and inboth spatial and temporal dimensions. We decompose theproblem into two steps, where the spatial noise is filteredout in the first step. The ballistic stroke boundaries are thendetected at the local curvature maxima, which we show to beinvariant to temporal sampling noise. We also propose a bag-of-strokes representation based on ballistic stroke segmentationfor online character recognition that improves the state-of-the-art recognition accuracies on multiple datasets