Department of Computer Science & Engineering

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IIT Jodhpur

We present here an idea of compression of user fed data from a touch screen input interface for storage and transmission over relatively lower bandwidth. The input is taken in the form of hand-written text, graphics, symbols or patterns and recorded as strokes in order of their temporal occurrence. The patterns are segmented into primitive forms each of which is then modeled with third order B-Spline Curves. The number of control points driving the Spline Curve is determined beforehand by recognizing the dominant points in the pattern. The significant reduction of redundancy in data can be exploited in wide application base including low-cost handheld device communication. This algorithm hence proposes a language independent tool for recording the handwriting of user in its original essence. Results obtained from preliminary testing on MATLAB and Android platform show significant improvement in compression ratio over the traditional storage and compression schemes. © 2011 IEEE.

World University root node

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

A compression scheme for handwritten patterns based on curve fitting

ACM International Conference Proceeding Series

Foreword

In this paper we propose a novel architecture for an interactive 3DTV system based on multiple uncalibrated cameras placed at general positions. The signal representation scheme proposed is compatible with the standard multi view coding framework making it amenable to using existing coding and compression algorithms. The proposed scheme also fits naturally to the concept of true 3DTV viewing experience where the viewer can choose a novel viewpoint based on the contents of the scene. © 2011 Springer-Verlag Berlin Heidelberg.

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

Uncalibrated camera based interactive 3DTV

A probe face image may contain noise due to environmental conditions, incorrect use of sensors or transmission error. The performance of face recognition severely depletes when the probe image is contaminated with noise. Denoising techniques can improve recognition performance, provided the correct parameters are used. In this paper, a parameter selection framework is presented. In the proposed framework, the optimal parameter set is selected for denoising using quality assessment algorithms with low complexity. Quality score based parameter selection is evaluated on the AR face dataset. A correlation study is discussed to ascertain the relationship between the quality scores and recognition rate. The experiments suggest that the proposed framework improves the performance both in terms of accuracy and computation time. © 2011 IEEE.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Quality assessment based denoising to improve face recognition performance

Recognizing faces with altered appearances is a challenging task and is only now beginning to be addressed by researchers. The paper presents an evolutionary granular approach for matching face images that have been altered by plastic surgery procedures. The algorithm extracts discriminating information from non-disjoint face granules obtained at different levels of granularity. At the first level of granularity, both pre and post-surgery face images are processed by Gaussian and Laplacian operators to obtain face granules at varying resolutions. The second level of granularity divides face image into horizontal and vertical face granules of varying size and information content. At the third level of granularity, face image is tessellated into non-overlapping local facial regions. An evolutionary approach is proposed using genetic algorithm to simultaneously optimize the selection of feature extractor for each face granule along with finding optimal weights corresponding to each face granule for matching. Experiments on pre and post-plastic surgery face images show that the proposed algorithm provides at least 15% better identification performance as compared to other face recognition algorithms. © 2011 IEEE.

2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops, FG 2011

Evolutionary granular approach for recognizing faces altered due to plastic surgery

Multibiometric systems fuse the evidence (e.g., match scores) pertaining to multiple biometric modalities or classifiers. Most score-level fusion schemes discussed in the literature require the processing (i.e., feature extraction and matching) of every modality prior to invoking the fusion scheme. This paper presents a framework for dynamic classifier selection and fusion based on the quality of the gallery and probe images associated with each modality with multiple classifiers. The quality assessment algorithm for each biometric modality computes a quality vector for the gallery and probe images that is used for classifier selection. These vectors are used to train Support Vector Machines (SVMs) for decision making. In the proposed framework, the biometric modalities are arranged sequentially such that the stronger biometric modality has higher priority for being processed. Since fusion is required only when all unimodal classifiers are rejected by the SVM classifiers, the average computational time of the proposed framework is significantly reduced. Experimental results on different multi-modal databases involving face and fingerprint show that the proposed quality-based classifier selection framework yields good performance even when the quality of the biometric sample is sub-optimal. © 2011 IEEE.

2011 International Joint Conference on Biometrics, IJCB 2011

A framework for quality-based biometric classifier selection

Over the years, automatic gender recognition has been used in many applications. However, limited research has been done on analyzing gender recognition across ethnicity scenario. This research aims at studying the performance of discriminant functions including Principal Component Analysis, Linear Discriminant Analysis and Subclass Discriminant Analysis with the availability of limited training database and unseen ethnicity variations. The experiments are performed on a heterogeneous database of 8112 images that includes variations in illumination, expression, minor pose and ethnicity. Contrary to existing literature, the results show that PCA provides comparable but slightly better performance compared to PCA+LDA, PCA+SDA and PCA+SVM. The results also suggest that linear discriminant functions provide good generalization capability even with limited number of training samples, principal components and with cross-ethnicity variations. © 2011 IEEE.

Is gender classification across ethnicity feasible using discriminant functions?

Video Data Management and Information Retrieval

Video Biometrics

The advent of multicore and many-core architectures saw them being deployed to speed-up computations across several disciplines and application areas. Prominent examples include semi-numerical algorithms such as sorting, graph algorithms, image processing, scientific computations, and the like. In particular, using GPUs for general purpose computations has attracted a lot of attention given that GPUs can deliver more than one TFLOP of computing power at very low prices. In this work, we use a new model of multicore computing called hybrid multicore computing where the computation is performed simultaneously a control device, such as a CPU, and an accelerator such as a GPU. To this end, we use two case studies to explore the algorithmic and analytical issues in hybrid multicore computing. Our case studies involve two different ways of designing hybrid multicore algorithms. The main contribution of this paper is to address the issues related to the design of hybrid solutions. We show our hybrid algorithm for list ranking is faster by 50% compared to the best known implementation [Z. Wei, J. JaJa; IPDPS 2010]. Similarly, our hybrid algorithm for graph connected components is faster by 25% compared to the best known GPU implementation [26]. © 2011 IEEE.

18th International Conference on High Performance Computing, HiPC 2011

Hybrid algorithms for list ranking and graph connected components

We are proposing a self deploying mobile sensor network which is empowered with event based relocation of redundant sensors for enhancing the quality of event sensing. Energy efficient Cell quorum based protocol is used for communication between event location and redundant sensors. Computationally light cascaded reorganization of sensor is suggested for relocation of sensors. The proposed method provides good coverage as it dynamically relocates the sensors either for avoiding coverage hole or for improving the quality of event sensing.