Sharat Chikkerur

Fingerprint enhancement using STFT analysis

Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition is still an open problem. Extracting features out of poor quality prints is the most challenging problem faced in this area. This paper introduces a new approach for fingerprint enhancement based on short time Fourier transform (STFT) Analysis. STFT is a well-known technique in signal processing to analyze non-stationary signals. Here we extend its application to 2D fingerprint images. The algorithm simultaneously estimates all the intrinsic properties of the fingerprints such as the foreground region mask, local ridge orientation and local ridge frequency. Furthermore we propose a probabilistic approach of robustly estimating these parameters. We experimentally compare the proposed approach to other filtering approaches in literature and show that our technique performs favorably.

What and where: A Bayesian inference theory of attention

In the theoretical framework of this paper, attention is part of the inference process that solves the visual recognition problem of what is where. The theory proposes a computational role for attention and leads to a model that predicts some of its main properties at the level of psychophysics and physiology. In our approach, the main goal of the visual system is to infer the identity and the position of objects in visual scenes: spatial attention emerges as a strategy to reduce the uncertainty in shape information while feature-based attention reduces the uncertainty in spatial information. Featural and spatial attention represent two distinct modes of a computational process solving the problem of recognizing and localizing objects, especially in difficult recognition tasks such as in cluttered natural scenes. We describe a specific computational model and relate it to the known functional anatomy of attention. We show that several well-known attentional phenomena--including bottom-up pop-out effects, multiplicative modulation of neuronal tuning curves and shift in contrast responses--all emerge naturally as predictions of the model. We also show that the Bayesian model predicts well human eye fixations (considered as a proxy for shifts of attention) in natural scenes.

Cancelable Biometrics: A Case Study in Fingerprints

Biometrics offers usability advantages over traditional token and password based authentication schemes, but raises privacy and security concerns. When compromised, credit cards and passwords can be revoked or replaced while biometrics are permanently associated with a user and cannot be replaced. Cancelable biometrics attempts to solve this by constructing revocable biometric templates. We present several constructs for cancelable templates using feature domain transformations and empirically examine their efficacy. We also present a method for accurate registration which is a key step in building cancelable transforms. The overall approach has been tested using large databases and our results demonstrate that without losing much accuracy, we can build a large number of cancelable transforms for fingerprints

K-plet and coupled BFS: a graph based fingerprint representation and matching algorithm

In this paper, we present a new fingerprint matching algorithm based on graph matching principles. We define a new representation called K-plet to encode the local neighborhood of each minutiae. We also present CBFS (Coupled BFS), a new dual graph traversal algorithm for consolidating all the local neighborhood matches and analyze its computational complexity. The proposed algorithm is robust to non-linear distortion. Ambiguities in minutiae pairings are solved by employing a dynamic programming based optimization approach. We present an experimental evaluation of the proposed approach and showed that it exceeds the performance of the NIST BOZORTH3 [3] matching algorithm.

Impact of singular point detection on fingerprint matching performance

A majority of the minutiae based fingerprint verification algorithms rely on explicit or implicit alignment of the minutiae points for matching the two prints. With no prior knowledge about point correspondences, this becomes a combinatorial problem. Global features of the fingerprints such as the core and delta points represent intrinsic points of reference that can be used to align the two prints and reduce the computational complexity of the matcher. However, automatic extraction of singular points is usually error prone and is therefore not used by existing matchers. But, a systematic study of the impact on matching performance when core/delta points are available has not been done to date. In this paper, we explore the effects of the availability of reliable core and delta points on speed and accuracy of a matching algorithm. Towards this end, we present significant improvements to core and delta point detection algorithm based on complex filtering principles originally proposed by Nilsson et al., (2005). We also present a modified graph based matching algorithm that can run in O(n) time when the reference points are available. We analyse the resulting improvement in computational complexity and present experimental evaluation over FVC2002 database. We show that there is upto 43% improvement (70.2 ms to 39.8 ms) in average verification time and almost no loss in accuracy when reliable core and delta points are used.

Fingerprint image enhancement using STFT analysis

Contrary to popular belief, despite decades of research in fingerprints, reliable fingerprint recognition is still an open problem. Extracting features out of poor quality prints is the most challenging problem faced in this area. This paper introduces a new approach for fingerprint enhancement based on Short Time Fourier Transform(STFT) Analysis. STFT is a well known technique in signal processing to analyze non-stationary signals. Here we extend its application to 2D fingerprint images.The algorithm simultaneously estimates all the intrinsic properties of the fingerprints such as the foreground region mask, local ridge orientation and local frequency orientation. We have evaluated the algorithm over a set of 800 images from FVC2002 DB3 database and obtained a 17% relative improvement in the recognition rate.

Efficient search and retrieval in biometric databases

Biometric identification has emerged as a reliable means of controlling access to both physical and virtual spaces. Fingerprints, face and voice biometrics are being increasingly used as alternatives to passwords, PINs and visual verification. In spite of the rapid proliferation of large-scale databases, the research has thus far been focused only on accuracy within small databases. In larger applications, response time and retrieval efficiency also become important in addition to accuracy. Unlike structured information such as text or numeric data that can be sorted, biometric data does not have any natural sorting order. Therefore indexing and binning of biometric databases represents a challenging problem. We present results using parallel combination of multiple biometrics to bin the database. Using hand geometry and signature features we show that the search space can be reduced to just 5% of the entire database.

Fingerprint Representation Using Localized Texture Features

Fingerprint representations can be broadly divided into three categories: image level, texture features and minutiae features. Both image based and texture based representations require accurate alignment before comparison. This presents a problem since accurate registration of fingerprints is challenging. On the other hand, minutiae based matchers are invariant to changes in orientation and position, but completely ignore the rich visual content in the image. In this paper, we present a localized texture based representation scheme that relies solely on visual content for identification and at the same time does not require absolute alignment. We outline techniques to efficiently compute these features and also propose an algorithm to perform identification based on these features. Our experimental evaluations over database of several sizes show that the proposed features are both accurate and scalable

Accent classification in speech

Apart form the word content and identity of a speaker; speech also conveys information about several soft biometric traits such as accent and gender. Accurate classification of these features can have a direct impact on present speech systems. An accent specific dictionary or word models can be used to improve accuracy of speech recognition systems. Gender and accent information can also be used to improve the performance of speaker recognition systems. In this paper, we distinguish between standard American English and Indian Accented English using the second and third formant frequencies of specific accent markers. A GMM classification is used on the feature set for each accent group. The results show that using just the formant frequencies of these accent markers is sufficient to achieve a suitable classification for these two accent groups.

Computer vision cracks the leaf code

Significance The botanical value of angiosperm leaf shape and venation (“leaf architecture”) is well known, but the astounding complexity and variation of leaves have thwarted efforts to access this underused resource. This challenge is central for paleobotany because most angiosperm fossils are isolated, unidentified leaves. We here demonstrate that a computer vision algorithm trained on several thousand images of diverse cleared leaves successfully learns leaf-architectural features, then categorizes novel specimens into natural botanical groups above the species level. The system also produces heat maps to display the locations of numerous novel, informative leaf characters in a visually intuitive way. With assistance from computer vision, the systematic and paleobotanical value of leaves is ready to increase significantly. Understanding the extremely variable, complex shape and venation characters of angiosperm leaves is one of the most challenging problems in botany. Machine learning offers opportunities to analyze large numbers of specimens, to discover novel leaf features of angiosperm clades that may have phylogenetic significance, and to use those characters to classify unknowns. Previous computer vision approaches have primarily focused on leaf identification at the species level. It remains an open question whether learning and classification are possible among major evolutionary groups such as families and orders, which usually contain hundreds to thousands of species each and exhibit many times the foliar variation of individual species. Here, we tested whether a computer vision algorithm could use a database of 7,597 leaf images from 2,001 genera to learn features of botanical families and orders, then classify novel images. The images are of cleared leaves, specimens that are chemically bleached, then stained to reveal venation. Machine learning was used to learn a codebook of visual elements representing leaf shape and venation patterns. The resulting automated system learned to classify images into families and orders with a success rate many times greater than chance. Of direct botanical interest, the responses of diagnostic features can be visualized on leaf images as heat maps, which are likely to prompt recognition and evolutionary interpretation of a wealth of novel morphological characters. With assistance from computer vision, leaves are poised to make numerous new contributions to systematic and paleobotanical studies.