Gaurav Aggarwal

University of Notre Dame, South Bend, Indiana, United States

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Publications (34)27.68 Total impact

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    ABSTRACT: Face images captured by surveillance cameras usually have poor resolution in addition to uncontrolled poses and illumination conditions, all of which adversely affect the performance of face matching algorithms. In this paper, we develop a completely automatic, novel approach for matching surveillance quality facial images to high-resolution images in frontal pose, which are often available during enrollment. The proposed approach uses multidimensional scaling to simultaneously transform the features from the poor quality probe images and the high-quality gallery images in such a manner that the distances between them approximate the distances had the probe images been captured in the same conditions as the gallery images. Tensor analysis is used for facial landmark localization in the low-resolution uncontrolled probe images for computing the features. Thorough evaluation on the Multi-PIE dataset and comparisons with state-of-the-art super-resolution and classifier-based approaches are performed to illustrate the usefulness of the proposed approach. Experiments on surveillance imagery further signify the applicability of the framework. We also show the usefulness of the proposed approach for the application of tracking and recognition in surveillance videos.
    IEEE Transactions on Software Engineering 12/2013; 35(12):3037-49. · 2.59 Impact Factor
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    ABSTRACT: Face images captured by surveillance cameras usually have poor resolution in addition to uncontrolled poses and illumination conditions, all of which adversely affect performance of face matching algorithms. In this paper, we develop a completely automatic, novel approach for matching surveillance quality facial images to high resolution images in frontal pose which are often available during enrollment. The proposed approach uses multidimensional scaling to simultaneously transform the features from the poor quality probe images and the high quality gallery images in such a manner that the distances between them approximate the distances had the probe images been captured in the same conditions as the gallery images. Tensor analysis is used for facial landmark localization in the low-resolution uncontrolled probe images for computing the features. Thorough evaluation on the Multi-PIE dataset [1] and comparisons with state-of-the-art super-resolution and classifier-based approaches are performed to illustrate the usefulness of the proposed approach. Experiments on surveillance imagery further signify the applicability of the framework. We also show the usefulness of the proposed approach for the application of tracking and recognition in surveillance videos.
    IEEE Transactions on Software Engineering 04/2013; · 2.59 Impact Factor
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    ABSTRACT: Identical twin face recognition is a challenging task due to the existence of a high degree of correlation in overall facial appearance. Commercial face recognition systems exhibit poor performance in differentiating between identical twins under practical conditions. In this paper, we study the usability of facial marks as biometric signatures to distinguish between identical twins. We propose a multiscale automatic facial mark detector based on a gradient-based operator known as the fast radial symmetry transform. The transform detects bright or dark regions with high radial symmetry at different scales. Next, the detections are tracked across scales to determine the prominence of facial marks. Extensive experiments are performed both on manually annotated and on automatically detected facial marks to evaluate the usefulness of facial marks as biometric signatures. Experiment results are based on identical twin images acquired at the 2009 Twins Days Festival in Twinsburg, Ohio. The results of our analysis signify the usefulness of the distribution of facial marks as a biometric signature. In addition, our results indicate the existence of some degree of correlation between geometric distribution of facial marks across identical twins.
    IEEE Transactions on Information Forensics and Security 01/2012; 7(5):1536-1550. · 1.90 Impact Factor
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    ABSTRACT: Several sources of variation in facial appearance that affect face matching performance have long been investigated. The recently introduced GBU challenge problem [1] indicates that there can be significant variation in performance across different partitions of the data, even when the impact of most known factors is eliminated or significantly reduced by the data collection and experimentation protocol. The GBU challenge problem consists of three partitions which are called the Good (easy to match), the Bad (average matching difficulty) and the Ugly (difficult to match). In this paper, we investigate various image and facial characteristics that can account for the observed significant difference in performance across these partitions. Given a match pair, we aim to predict the partition it belongs to. Partial Least Squares (PLS)-based regression is used to perform the prediction task. Our analysis indicates that the match pairs from the three partitions differ from each other in terms of simple but often ignored factors like image sharpness, hue, saturation and extent of facial expressions.
    Applications of Computer Vision (WACV), 2012 IEEE Workshop on; 01/2012
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    ABSTRACT: Plastic surgery procedures can significantly alter facial appearance, thereby posing a serious challenge even to the state-of-the-art face matching algorithms. In this paper, we propose a novel approach to address the challenges involved in automatic matching of faces across plastic surgery variations. In the proposed formulation, part-wise facial characterization is combined with the recently popular sparse representation approach to address these challenges. The sparse representation approach requires several images per subject in the gallery to function effectively which is often not available in several use-cases, as in the problem we address in this work. The proposed formulation utilizes images from sequestered non-gallery subjects with similar local facial characteristics to fulfill this requirement. Extensive experiments conducted on a recently introduced plastic surgery database [17] consisting of 900 subjects highlight the effectiveness of the proposed approach.
    Applications of Computer Vision (WACV), 2012 IEEE Workshop on; 01/2012
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    ABSTRACT: The performance of face recognition algorithms is affected both by external factors and internal subject characteristics [1]. Reliably identifying these factors and understanding their behavior on performance can potentially serve two important goals - to predict the performance of the algorithms at novel deployment sites and to design appropriate acquisition environments at prospective sites to optimize performance. There have been a few recent efforts in this direction that focus on identifying factors that affect face recognition performance but there has been no extensive study regarding the consistency of the effects various factors have on algorithms when other covariates vary. To give an example, a smiling target image has been reported to be better than a neutral expression image, but is this true across all possible illumination conditions, head poses, gender, etc.? In this paper, we perform rigorous experiments to provide answers to such questions. Our investigation indicates that controlled lighting and smiling expression are the most favorable conditions that consistently give superior performance even when other factors are allowed to vary. We also observe that internal subject characterization using biometric menagerie-based classification shows very weak consistency when external conditions are allowed to vary.
    Proceedings of the 2011 International Joint Conference on Biometrics; 10/2011
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    ABSTRACT: Predicting performance of face recognition systems on previously unseen data is very useful for deploying these systems in different places. Different extrinsic and intrinsic factors like illumination, pose, expression, etc. affect matching performance of even the best of face recognition algorithms. This makes it difficult for one to accurately predict how a system will perform at a new deployment location with novel imaging conditions. With this motivation, we present a novel framework to predict performance of face matching systems on unseen data without the need of subject-wise labeling of images typically necessary for evaluations. The framework relies on learning a mapping from a space characterizing imaging conditions to the score space using Multi-dimensional Scaling. Extensive evaluation on the Multi-PIE data using different algorithms demonstrates the usefulness of the prediction framework. Experiments using training data which is completely different from the test data further justifies the use of the proposed approach for the task of performance prediction.
    Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on; 07/2011
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    Soma Biswas, Gaurav Aggarwal, Patrick J. Flynn
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    ABSTRACT: Low-resolution surveillance videos with uncontrolled pose and illumination present a significant challenge to both face tracking and recognition algorithms. Considerable appearance difference between the probe videos and high-resolution controlled images in the gallery acquired during enrollment makes the problem even harder. In this paper, we extend the simultaneous tracking and recognition framework [22] to address the problem of matching high-resolution gallery images with surveillance quality probe videos. We propose using a learning-based likelihood measurement model to handle the large appearance and resolution difference between the gallery images and probe videos. The measurement model consists of a mapping which transforms the gallery and probe features to a space in which their inter-Euclidean distances approximate the distances that would have been obtained had all the descriptors been computed from good quality frontal images. Experimental results on real surveillance quality videos and comparisons with related approaches show the effectiveness of the proposed framework.
    01/2011;
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    ABSTRACT: There exists a high degree of similarity in facial appearance between identical twins that makes it difficult for even the state of the art face matching systems to distinguish between them. Given the consistent increase in the number of twin births in recent decades, there is a need to develop alternate approaches to characterize facial appearance that can address this challenging task that has eluded even humans. In this paper, we investigate the usefulness of facial marks as biometric signatures with focus on the task of distinguishing between identical twins. We define and characterize a set of facial marks that are manually annotated by multiple observers. The geometric distribution of annotated facial marks along with their respective categories is used to characterize twin face images. The analysis is conducted on 295 twin face images acquired at the Twins Days Festival at Twinsburg, Ohio, in 2009. The results of our analysis signify the usefulness of distribution of facial marks as a biometric signature. In addition, contrary to prior research, our results indicate the existence of some degree of correlation between positions of facial marks belonging to identical twins.
    Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on; 01/2011
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    Soma Biswas, Gaurav Aggarwal, Rama Chellappa
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    ABSTRACT: Many shape matching methods are either fast but too simplistic to give the desired performance or promising as far as performance is concerned but computationally demanding. In this paper, we present a very simple and efficient approach that not only performs almost as good as many state-of-the-art techniques but also scales up to large databases. In the proposed approach, each shape is indexed based on a variety of simple and easily computable features which are invariant to articulations, rigid transformations, etc. The features characterize pairwise geometric relationships between interest points on the shape. The fact that each shape is represented using a number of distributed features instead of a single global feature that captures the shape in its entirety provides robustness to the approach. Shapes in the database are ordered according to their similarity with the query shape and similar shapes are retrieved using an efficient scheme which does not involve costly operations like shape-wise alignment or establishing correspondences. Depending on the application, the approach can be used directly for matching or as a first step for obtaining a short list of candidate shapes for more rigorous matching. We show that the features proposed to perform shape indexing can be used to perform the rigorous matching as well, to further improve the retrieval performance.
    IEEE Transactions on Multimedia 09/2010; · 1.75 Impact Factor
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    Soma Biswas, Gaurav Aggarwal, Rama Chellappa
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    ABSTRACT: We present a nonstationary stochastic filtering framework for the task of albedo estimation from a single image. There are several approaches in the literature for albedo estimation, but few include the errors in estimates of surface normals and light source direction to improve the albedo estimate. The proposed approach effectively utilizes the error statistics of surface normals and illumination direction for robust estimation of albedo, for images illuminated by single and multiple light sources. The albedo estimate obtained is subsequently used to generate albedo-free normalized images for recovering the shape of an object. Traditional Shape-from-Shading (SFS) approaches often assume constant/piecewise constant albedo and known light source direction to recover the underlying shape. Using the estimated albedo, the general problem of estimating the shape of an object with varying albedo map and unknown illumination source is reduced to one that can be handled by traditional SFS approaches. Experimental results are provided to show the effectiveness of the approach and its application to illumination-invariant matching and shape recovery. The estimated albedo maps are compared with the ground truth. The maps are used as illumination-invariant signatures for the task of face recognition across illumination variations. The recognition results obtained compare well with the current state-of-the-art approaches. Impressive shape recovery results are obtained using images downloaded from the Web with little control over imaging conditions. The recovered shapes are also used to synthesize novel views under novel illumination conditions.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 06/2009; 31(5):884-99. · 4.80 Impact Factor
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    ABSTRACT: Today's embedded computing applications are characterised by increased functionality, and hence increased design complexity and processing requirements. The resulting design spaces are vast and designers are typically able to evaluate only small subsets of solutions due to lack of efficient design tools. In this paper, we propose an architectural level design methodology that provides a means for a comprehensive design space exploration for smart camera applications and enable designers to select higher quality solutions and provides substantial savings on the overall cost of the system. We present efficient, accurate and intuitive models for performance estimation and validate them with experiments.
    IJES. 01/2009; 4:83-97.
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    ABSTRACT: Though a lot of research has been done to match fingerprints, most existing approaches rely on locations of minutiae features for matching tasks. Relatively, little effort has gone into utilizing textural information present in fingerprints as distinguishing characteristic. In this paper, we propose a novel gradient-based approach to characterize textural information in fingerprints for the task of biometric matching. In particular, the proposed approach uses histograms of oriented gradients (HOGs) to represent minutiae neighborhoods. The minutiae neighborhoods are divided into several regions to make the computed histograms distinguishing and robust at the same time. Experimental results are provided to show the efficacy of the proposed characterization.
    Biometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on; 11/2008
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    ABSTRACT: One of the main challenges in building an efficient and scalable automatic fingerprint identification system is to identify features which are highly discriminative and are reproducible across different prints of the same finger. Most existing fingerprint matching approaches rely on minutiae geometry. Relatively, little effort has gone into analyzing ridge flow patterns present in the fingerprint, partly due to difficulty in extracting robust discriminative features from the fingerprint images. In this paper, we analyze the usefulness of ridge curvature information for fingerprint matching and classification applications. Specifically, for an indexing framework, we explore whether the curvature information can be utilized along with the existing minutiae geometry-based features for further reducing the number of potential candidates for fingerprint identification. Experimental results indicate the robustness of the proposed curvature-based characterization and its usefulness in improving the efficiency of existing fingerprint-based identification systems.
    Biometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on; 11/2008
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    ABSTRACT: Human faces undergo a lot of change in appearance as they age. Though facial aging has been studied for decades, it is only recently that attempts have been made to address the problem from a computational point of view. Most of these early efforts follow a simulation approach in which matching is performed by synthesizing face images at the target age. Given the innumerable different ways in which a face can potentially age, the synthesized aged image may not be similar to the actual aged image. In this paper, we bypass the synthesis step and directly analyze the drifts of facial features with aging from a purely matching perspective. Our analysis is based on the observation that facial appearance changes in a coherent manner as people age. We provide measures to capture this coherency in feature drifts. Illustrations and experimental results show the efficacy of such an approach for matching faces across age progression.
    Biometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on; 11/2008
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    ABSTRACT: Biometric matching decisions have traditionally been made based solely on a score that represents the similarity of the query biometric to the enrolled biometric(s) of the claimed identity. Fusion schemes have been proposed to benefit from the availability of multiple biometric samples (e.g., multiple samples of the same fingerprint) or multiple different biometrics (e.g., face and fingerprint). These commonly adopted fusion approaches rarely make use of the large number of non-matching biometric samples available in the database in the form of other enrolled identities or training data. In this paper, we study the impact of combining this information with the existing fusion methodologies in a cohort analysis framework. Experimental results are provided to show the usefulness of such a cohort-based fusion of face and fingerprint biometrics.
    Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on; 05/2008
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    ABSTRACT: We present a face reconstruction approach for revocable face matching. The proposed approach generates photometrically valid cancelable face images by following the image formation process. Given a face image, the approach estimates facial albedo followed by a subject-specific key based photometric deformation to generate a cancelable face image. The proposed approach allows for using any available face matcher to perform verification or recognition in the transformed domain, a capability missing from most existing works on cancelable face matching. Experiments are performed to evaluate the performance, privacy and cancelable aspects of the face images reconstructed using the approach. Results obtained are very promising and make a strong case for such backward compatible cancelable face representations that can seamlessly make use of advancements in automatic face recognition research.
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2008, March 30 - April 4, 2008, Caesars Palace, Las Vegas, Nevada, USA; 01/2008
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    Conference Paper: Video Biometrics
    R. Chellappa, G. Aggarwal
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    ABSTRACT: A strong requirement to come up with secure and user- friendly ways to authenticate and identify people, to safeguard their rights and interests, has probably been the main guiding force behind biometrics research. Though a vast amount of research has been done to recognize humans based on still images, the problem is still far from solved for unconstrained scenarios. This has led to an increased interest in using video for the task of biometric recognition. Not only does video provide more information, but also is more suitable for recognizing humans in general surveillance scenarios. Other than the multitude of still frames, video makes it possible to characterize biometrics based on inherent dynamics like gait which is not possible with still images. In this paper, we describe several recent algorithms to illustrate the usefulness of videos to identify humans. A brief discussion on remaining challenges is also included.
    Image Analysis and Processing, 2007. ICIAP 2007. 14th International Conference on; 10/2007
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    ABSTRACT: Traditional photometric stereo algorithms employ a Lambertian reflectance model with a varying albedo field and involve the appearance of only one object. In this paper, we generalize photometric stereo algorithms to handle all appearances of all objects in a class, in particular the human face class, by making use of the linear Lambertian property. A linear Lambertian object is one which is linearly spanned by a set of basis objects and has a Lambertian surface. The linear property leads to a rank constraint and, consequently, a factorization of an observation matrix that consists of exemplar images of different objects (e.g., faces of different subjects) under different, unknown illuminations. Integrability and symmetry constraints are used to fully recover the subspace bases using a novel linearized algorithm that takes the varying albedo field into account. The effectiveness of the linear Lambertian property is further investigated by using it for the problem of illumination-invariant face recognition using just one image. Attached shadows are incorporated in the model by a careful treatment of the inherent nonlinearity in Lambert's law. This enables us to extend our algorithm to perform face recognition in the presence of multiple illumination sources. Experimental results using standard data sets are presented.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 03/2007; 29(2):230-45. · 4.80 Impact Factor
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    Soma Biswas, Gaurav Aggarwal, Rama Chellappa
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    ABSTRACT: Most shapematchingmethodsare either fast but toosim- plistic to give the desired performance or promising as far as performance is concerned but computationally demand- ing. In this paper, we present a very simple and efficient ap- proachthatnotonly performsalmost asgoodasmanystate- of-the-art techniques but also scales up to large databases. In the proposed approach, each shape is indexed based on a variety of simple and easily computable features which are invariant to articulations and rigid transformations. The features characterize pairwise geometric relationships be- tween interest points on the shape, thereby providing ro- bustness to the approach. Shapes are retrieved using an efficient scheme which does not involve costly operations like shape-wise alignment or establishing correspondences. Even for a moderate size database of 1000 shapes, the re- trieval process is several times faster than most techniques with similar performance. Extensive experimental results are presented to illustrate the advantages of our approach as compared to the best in the field.
    2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), 18-23 June 2007, Minneapolis, Minnesota, USA; 01/2007

Publication Stats

287 Citations
27.68 Total Impact Points

Institutions

  • 2011–2013
    • University of Notre Dame
      • Department of Computer Science and Engineering
      South Bend, Indiana, United States
  • 2004–2010
    • University of Maryland, College Park
      • • Department of Electrical & Computer Engineering
      • • Center for Automation Research (CfAR)
      • • Department of Computer Science
      College Park, MD, United States
  • 2005
    • Loyola University Maryland
      Baltimore, Maryland, United States