Department of Computer Science & Engineering

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IIT Jodhpur

In this paper, we propose a novel feature representation for binary patterns by exploiting the object shape information. Initial evaluation of the representation is performed for Bengali and Gujarati script character classification. The extension of the representation for word images is presented subsequently. The proposed feature representation in combination with distance-based hashing is applied for defining novel word image-based document image indexing and retrieval framework. The concept of hierarchical hashing is utilized to reduce the retrieval time complexity. In addition, with the objective of reduction in the size of hashing data structure, the concept of multi-probe hashing is extended for binary mapping functions. The exhaustive experimental evaluation of the proposed framework on a collection of documents belonging to Devanagari, Bengali and English scripts has yielded encouraging results. © 2012 Springer-Verlag.

World University root node

International Journal on Document Analysis and Recognition

Word shape descriptor-based document image indexing: A new DBH-based approach

In recent years there has been a growing interest in inferring social relations amongst actors in a video using audiovisual features, co-appearance features or both. The discovered relations between actors have been used for identifying leading roles, detecting rival communities in a movie plot etc. In this paper we propose an unsupervised method which uses the video's transcript and closed caption information for discovering actor communities (group of actors or characters in a film that share a common perspective/viewpoint on an issue) from videos. The method proposed groups together actors using a topic model based approach, which jointly models actor-actor interaction (two actors interact when they share the same scene) and the topics associated with their conversations/dialogs. This joint modeling approach shows encouraging results compared to existing methods. Copyright © 2013 ISCA.

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Inferring actor communities from videos

The paper presents a novel script independent CRF based inferencing framework for character recognition. In this framework we consider a word as a sequence of connected components. The connected components are obtained using different binarization schemes and different possible sequences are considered using a tree structure. CRF uses contextual information to learn perfect primitive sequences and finds the most probable labeling of the sequence of primitives using multiple hypothesis tree to form the correct sequence of alphabets. This approach is particularly suitable for degraded printed document images as it considers multiple alternate hypotheses for correct decision. © 2013 IEEE.

Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

Character recognition using conditional random field based recognition engine

Sparse representation based image restoration techniques 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. © 2013 IEEE.

Sparse document image coding for restoration

For a robust face biometric system, a reliable anti-spoofing approach must be deployed to circumvent the print and replay attacks. Several techniques have been proposed to counter face spoofing, however a robust solution that is computationally efficient is still unavailable. This paper presents a new approach for spoofing detection in face videos using motion magnification. Eulerian motion magnification approach is used to enhance the facial expressions commonly exhibited by subjects in a captured video. Next, two types of feature extraction algorithms are proposed: (i) a configuration of LBP that provides improved performance compared to other computationally expensive texture based approaches and (ii) motion estimation approach using HOOF descriptor. On the Print Attack and Replay Attack spoofing datasets, the proposed framework improves the state-of-art performance, especially HOOF descriptor yielding a near perfect half total error rate of 0%and 1.25% respectively. © 2013 IEEE.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Computationally efficient face spoofing detection with motion magnification

HEp-2 cell image classification is an important and relatively unexplored area of research. This paper presents an experimental analysis of five different categories of feature sets with four different classifiers to determine the best performing combination of features and classifiers. The analysis is performed on the ICIP 2013 Cell Image Classification Contest Training dataset comprising over 13,000 cell images pertaining to six cell classes. The results computed with 10 fold cross validation show that texture features perform the best among all the explored feature sets and the combination of Laws features with SVM yields the highest accuracy. © 2013 Springer International Publishing.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

HEp-2 cell image classification: A comparative analysis

Determination of 3-D data from the images is important in the field of machine vision. The most direct way to achieve this is stereo vision. 3-D data obtained from stereo images form the basis for many higher level tasks like robot navigation, object recognition, 3-D modeling, path planning etc. in the field of interplanetary mission, hazardous environments, deep sea exploration, autonomous car driving. In this work, we attempt to recover the 3-D information from the stereo images for developing the vision based navigation of future planetary rover missions. This paper details the process flow and algorithm for the retrieval of 3-D information from the images obtained from our stereo rig. The results obtained in this work closely matches with ground truth. © 2013 IEEE.

Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013

3D information retrieval for visual odometry system of planetary exploration rovers - A stereo vision approach

Iris as a biometric identifier is assumed to be stable over a period of time. However, some researchers have observed that for long time lapse, the genuine match score distribution shifts towards the impostor score distribution and the performance of iris recognition reduces. The main purpose of this study is to determine if the shift in genuine scores can be attributed to aging or not. The experiments are performed on the two publicly available iris aging databases namely, ND-Iris-Template-Aging-2008-2010 and ND-TimeLapseIris-2012 using a commercial matcher, VeriEye. While existing results are correct about increase in false rejection over time, we observe that it is primarily due to the presence of other covariates such as blur, noise, occlusion, and pupil dilation. This claim is substantiated with quality score comparison of the gallery and probe pairs. © 2013 Mehrotra et al.

Fulltext

PLoS ONE

Does iris change over time?

Over the years, iris recognition has gained importance in the biometrics applications and is being used in several large scale nationwide projects. Though iris patterns are unique, they may be affected by external factors such as illumination, camera-eye angle, and sensor interoperability. The presence of contact lens, particularly color cosmetic lens, may also pose a challenge to iris biometrics as it obfuscates the iris patterns and changes the inter and intra-class distributions. This paper presents an in-depth analysis of the effect of contact lens on iris recognition performance. We also present the IIIT-D Contact Lens Iris database with over 6500 images pertaining to 101 subjects. For each subject, images are captured without lens, transparent (prescription) lens, and color cosmetic lens (textured) using two different iris sensors. The results computed using VeriEye suggest that color cosmetic lens sigmficantly increases the false rejection at a fixed false acceptance rate. Also, the experiments on four existing lens detection algorithms suggest that incorporating lens detection helps in maintaining the iris recognition performance. However further research is required to build sophisticated lens detection algorithm that can improve iris recognition. © 2013 IEEE.

Proceedings - 2013 International Conference on Biometrics, ICB 2013

Revisiting iris recognition with color cosmetic contact lenses

Sorting has been a topic of immense research value since the inception of Computer Science. Hybrid computing on multicore architectures involves computing simultaneously on a tightly coupled heterogeneous collection of devices. In this work, we consider a multicore CPU along with a many core GPU as our experimental hybrid platform. In this work, we present a hybrid comparison based sorting algorithm which utilizes a many-core GPU and a multi-core CPU to perform sorting. The algorithm is broadly based on splitting the input list according to a large number of splitters followed by creating independent sub lists. Sorting the independent sub lists results in sorting the entire original list. On a CPU+GPU platform consisting of an Intel i7 980 and an Nvidia GTX 580, our algorithm achieves a 20% gain over the current best known comparison sort result that was published by Davidson et. al. [In Par 2012]. On the above experimental platform, our results are better by 40% on average over a similar GPU-Alone algorithm proposed by Leischner et. al. [IPDPS 2010]. Our results also show that our algorithm and its implementation scale with the size of the input. We also show that such performance gains can be obtained on other hybrid CPU+GPU platforms. © 2013 IEEE.