Department of Computer Science & Engineering

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IIT Jodhpur

Challenges in automatic face recognition can be classified in several categories such as illumination, image quality, expression, pose, aging, and disguise. In this chapter, the authors focus on recognizing face images with disguise variations. Even though face recognition with disguise variations is a major challenge, the research studies on this topic are limited. In this study, first disguise variations are defined followed by an overview of the existing databases used for disguise analysis. Next, the studies that are dedicated to the impact of disguise variations on existing face recognition techniques are introduced. Finally, a collection of several techniques proposed in state-of-the-art which are robust against disguise variations is provided. This study shows that disguise variations have a significant impact on face recognition; hence, more robust approaches are required to address this important challenge. © 2014 by IGI Global. All rights reserved.

World University root node

Face Recognition in Adverse Conditions

Recognizing face images with disguise variations

Automatic fingerprint verification systems use ridge flow patterns and general morphological information for broad classification and minutia features for verification. With the availability of high resolution fingerprint sensors, it is now feasible to capture more intricate features such as ridges, pores, permanent scars, and incipient ridges. These fine details are characterized as level-3 or extended features and play an important role in matching and improving the verification accuracy. The main objective of this research is to develop a quality induced multi-classifier fingerprint verification algorithm that incorporates both level-2 and level-3 features. A quality assessment algorithm is developed that uses Redundant Discrete Wavelet Transform to extract edge, noise and smoothness information in local regions and encodes into a quality vector. The feature extraction algorithm first registers the gallery and probe fingerprint images using a two-stage registration process. Then, a fast Mumford-Shah curve evolution algorithm is used to extract four level-3 features namely, pores, ridge contours, dots, and incipient ridges. Gallery and probe features are matched using Mahalanobis distance measure and quality based likelihood ratio approach. Further, the quality induced sum rule fusion algorithm is used to combine the match scores obtained from level-2 and level-3 features. The experiments performed on 1000 ppi (pixels per inch) fingerprint databases show the effectiveness of the proposed algorithms. © 2014 Springer-Verlag Berlin Heidelberg.

Lecture Notes in Electrical Engineering

Quality induced multiclassifier fingerprint verification using extended feature set

In this paper we propose SEVA (Salient Events for Video Analytics), a framework for accurate detection and localization of salient events from a given video shot. Our proposed method is based on statistical learning theory and models salient event detection as a one-class classification problem. Video frames are split into blocks for extracting both the spatial and temporal features. Given a video shots we first track the moving foreground blob. Features are extracted using only the foreground pixels to avoid influence of the background. Using Support Vector Data Description (SVDD) in kernel feature space for each block in a given video frame, the decision boundary for the normal activity class is modeled. For a test video sequence, feature vectors are computed from the video frames and the learnt model is utilized to classify each block as normal or salient. Finally, we have adapted a spatio-temporal smoothing approach to remove the false positives. We have reported both qualitative and quantitative results of our experiments on two real-world benchmarked video datasets. Performance of SEVA is compared with five recent works on video event detection to validate its effectiveness. © 2014 IEEE.

Proceedings - 4th International Conference on Emerging Applications of Information Technology, EAIT 2014

SEVA: A salient event detection framework from video shots using support vector data description

Histopathological grading of cancer is a measure of the cell appearance in malignant neoplasms. Grading offers an in-sight to the growth of the cancer and helps in developing individual treatment plans. The Nottingham grading system [12], well known method for invasive breast cancer grading, primarily relies on the mitosis count in histopathological slides. Pathologists manually identify mitotic figures from a few thousand slide images for each patient to determine the grade of the cancer. Mitotic figures are hard to identify as the appearance of the mitotic cells change at different phases of mitosis. So, the manual cancer grading is not only a tedious job but also prone to observer variability. We propose a fast and accurate approach for automatic mitosis detection from histopathological images using an enhanced random forest classifier with weighted random trees. The random trees are assigned a tree penalty and a forest penalty depending on their classification performance at the training phase. The weight of a tree is calculated based on these penalties. The forest is trained through regeneration of population from weighted trees. The input data is classified based on weighted voting from the random trees after several populations. Experiments show at least 11 percent improvement in F1 score on more than 450 histopathological images at ×40 magnification. Copyright 2014 ACM.

ACM International Conference Proceeding Series

Enhanced random forest for mitosis detection

Latent fingerprint identification is of critical importance in criminal investigation. FBI's Next Generation Identification program demands latent fingerprint identification to be performed in lights-out mode, with very little or no human intervention. However, the performance of an automated latent fingerprint identification is limited due to imprecise automated feature (minutiae) extraction, specifically due to noisy ridge pattern and presence of background noise. In this paper, we propose a novel descriptor based minutiae detection algorithm for latent fingerprints. Minutia and non-minutia descriptors are learnt from a large number of tenprint fingerprint patches using stacked denoising sparse autoencoders. Latent fingerprint minutiae extraction is then posed as a binary classification problem to classify patches as minutia or non-minutia patch. Experiments performed on the NIST SD-27 database shows promising results on latent fingerprint matching. © 2014 IEEE.

IJCB 2014 - 2014 IEEE/IAPR International Joint Conference on Biometrics

On latent fingerprint minutiae extraction using stacked denoising sparse AutoEncoders

The Geographic Perimeter Stateless Routing (GPSR)use location information to forward packets greedily. Nodes need to keep only this local information, hence called as stateless. When Greedy Forwarding is fails, the algorithm recovers by switching to face routing, which is based on the right-hand rule in planarized node graph, to route around the void. The Gabriel Graph(GG) and the Relative Neighbourhood Graph (RNG) are the two graphs used for planarization but may lead to graph partitioning. Alternatively, instead of switching to face routing and planarizition, the existing Greedy Distributed Spanning Tree Routing(GDSTR) scheme enables nodes to continue routing by traversing on a spanning tree until it reaches a point where greedy forwarding can take place. GDSTR sometimes incorporates two spanning trees although it uses only one. So, in this work, we proposed new tree selection algorithm in GDSTR which improves the performance in terms of path and hop stretch with the competitive schemes. © 2014 IEEE.

2014 Recent Advances in Engineering and Computational Sciences, RAECS 2014

Improvised tree selection algorithm in Greedy Distributed Spanning Tree Routing

Building bilateral semantic associations between images and texts is among the fundamental problems in computer vision. In this paper, we study two complementary cross-modal prediction tasks: (i) predicting text(s) given an image ("Im2Text"), and (ii) predicting image(s) given a piece of text ("Text2Im"). We make no assumption on the specific form of text; i.e., it could be either a set of labels, phrases, or even captions. We pose both these tasks in a retrieval framework. For Im2Text, given a query image, our goal is to retrieve a ranked list of semantically relevant texts from an independent textcorpus (i.e., texts with no corresponding images). Similarly, for Text2Im, given a query text, we aim to retrieve a ranked list of semantically relevant images from a collection of unannotated images (i.e., images without any associated textual meta-data). We propose a novel Structural SVM based unified formulation for these two tasks. For both visual and textual data, two types of representations are investigated. These are based on: (1) unimodal probability distributions over topics learned using latent Dirichlet allocation, and (2) explicitly learned multi-modal correlations using canonical correlation analysis. Extensive experiments on three popular datasets (two medium and one web-scale) demonstrate that our framework gives promising results compared to existing models under various settings, thus confirming its efficacy for both the tasks. © 2014. The copyright of this document resides with its authors.

BMVC 2014 - Proceedings of the British Machine Vision Conference 2014

Im2Text and Text2Im: Associating images and texts for cross-modal retrieval

In a knowledge based service system like IT services, the requirements of skills to service customer requests keep changing with time. The service workers are expected to learn the required skills very quickly and become productive. Due to high attrition rate and demand, service workers are given basic class room training and then rest of the training is carried out on-job. When a service worker learns multiple skills simultaneously, learning slows down due to factors like forgetting and interference. At the same time, the organization needs to meet service level agreements (SLA). We have developed a model for on-job training which extends the business process for IT service delivery. The key idea is to model learning, forgetting and interference in service time estimation to get realistic service times. Accurate estimation of service time taken by a service worker to resolve the service tickets helps in resource allocation and planning decisions for achieving the desired objectives of upskilling and SLA success. The simulation of execution of the augmented business process provides insights into what kind of planning and dispatch policies should be practiced for achieving the desired goals of multi-skill learning and SLA success. © Springer-Verlag Berlin Heidelberg 2014.

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

How to enable multiple skill learning in a SLA constrained service system?

Modern urban areas constitute of rich mix of mobile user devices enabled with sensors and capability of being continuously connected to existing social networking infrastructure. In future the cities can act as single sociotechnical super-organism with capability to generate large scale adaptive urban dynamics expressing various forms of urban intelligence. Such an advanced situation-aware super organism will be difficult to manage with traditional approaches and there is a need for innovative mechanisms for coordination in such open, unpredictable and dynamic pervasive environment. In this paper we propose a novel unified model of bio-inspired mechanism for modeling spatially situated pervasive computing scenarios and exploring emergence of self-adaptive behaviors in individuals within urban super organism. Simulation results demonstrate efficacy of the proposed approach. © 2014 IEEE.

Proceedings - 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2014

Exploring bio-inspired self-organization for developing emergent intelligence in urban super organism

To exhibit multi-modal information and to facilitate people in finding multimedia resources, tagging plays a significant role. Various public events like protests and demonstrations are always consequences of break out of some public outrage resulting from prolonged exploitation and harassment. This outrage can be seen in news footage, blogs, text news and other web data. So, aggregating this variety of data from heterogeneous sources is a prerequisite step for tagging multimedia data with appropriate content. Since content has no meaning without a context, a video should be tagged with its relevant context and content information to assist user in multimedia retrieval. This paper proposes a model for tagging of multimedia data on the basis of contextual meaning. Since context is knowledge based, it has to be guided and learned by ontology which will help fragmented information to be represented in a more meaningful way. Our tagging approach is novel and has practical applicability in the sense that whenever a new video is uploaded on some media sharing site, the context and content information gets attached automatically to a video. Thus, providing relatively complete information associated with the video. © 2014 IEEE.