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Proceedings ArticleDOI

Evolutionary granular approach for recognizing faces altered due to plastic surgery

TL;DR: An evolutionary granular approach for matching face images that have been altered by plastic surgery procedures 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 facegranule for matching.
Abstract: 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.
Citations
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Proceedings ArticleDOI
06 Dec 2012
TL;DR: The impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup is studied and it is suggested that this simple alteration can indeed compromise the accuracy of a biometric system.
Abstract: The matching performance of automated face recognition has significantly improved over the past decade. At the same time several challenges remain that significantly affect the deployment of such systems in security applications. In this work, we study the impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup. Towards understanding its impact, we first assemble two databases containing face images of subjects, before and after applying makeup. We present experimental results on both databases that reveal the effect of makeup on automated face recognition and suggest that this simple alteration can indeed compromise the accuracy of a bio-metric system. While these are early results, our findings clearly indicate the need for a better understanding of this face altering scheme and the importance of designing algorithms that can successfully overcome the obstacle imposed by the application of facial makeup.

162 citations


Cites background from "Evolutionary granular approach for ..."

  • ...Recent work has focused on the impact of plastic surgery on face recognition [1][14][13][3]....

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Book ChapterDOI
01 Jan 2016
TL;DR: This chapter analyzes the effects of intentional or unintentional face image alterations on face recognition algorithms and the human capabilities to deal with altered images in scenarios where the user template is created from printed photographs rather than from images acquired live during enrollment.
Abstract: Face recognition in controlled environments is nowadays considered rather reliable, and if face is acquired in proper conditions, a good accuracy level can be achieved by state-of-the-art systems. However, we show that, even under these desirable conditions, some intentional or unintentional face image alterations can significantly affect the recognition performance. In particular, in scenarios where the user template is created from printed photographs rather than from images acquired live during enrollment (e.g., identity documents ), digital image alterations can severely affect the recognition results. In this chapter, we analyze both the effects of such alterations on face recognition algorithms and the human capabilities to deal with altered images.

108 citations

Proceedings ArticleDOI
06 Dec 2012
TL;DR: A fusion approach is proposed that combines information from the face and ocular regions to enhance recognition performance in the identification mode, and provides the highest reported recognition performance on a publicly accessible plastic surgery database.
Abstract: The task of successfully matching face images obtained before and after plastic surgery is a challenging problem. The degree to which a face is altered depends on the type and number of plastic surgeries performed, and it is difficult to model such variations. Existing approaches use learning based methods that are either computationally expensive or rely on a set of training images. In this work, a fusion approach is proposed that combines information from the face and ocular regions to enhance recognition performance in the identification mode. The proposed approach provides the highest reported recognition performance on a publicly accessible plastic surgery database, with a rank-one accuracy of 87.4%. Compared to existing approaches, the proposed approach is not learning based and reduces computational requirements. Furthermore, a systematic study of the matching accuracies corresponding to various types of surgeries is presented.

61 citations


Cites background or methods from "Evolutionary granular approach for ..."

  • ...[3] used an evolutionary granular approach with CLBP and SURF features to process tessellated face images....

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  • ...Only recently, have researchers from the biometric community begun to investigate the effect of plastic surgery on face recognition algorithms [13, 3, 1]....

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Journal ArticleDOI
TL;DR: A survey of the state of the art on face recognition, starting by an analysis of the diffusion of the facial plastic surgery and describing the key aspects of each of the most statistically relevant treatments available, resumed by a synthetic table.

45 citations


Cites background from "Evolutionary granular approach for ..."

  • ...in [40], proposed a complex, multiresolution approach to analyze the face images at different spatial frequency scales....

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Book ChapterDOI
05 Nov 2012
TL;DR: An ensemble of Gabor Patch classifiers via Rank-Order list Fusion (GPROF) is proposed, inspired by the assumption of the interior consistency of face components in terms of identity, to address FRAPS problem.
Abstract: It has been observed that many face recognition algorithms fail to recognize faces after plastic surgery, which thus poses a new challenge to automatic face recognition. This paper first gives a comprehensive study on Face Recognition After Plastic Surgery (FRAPS), with careful analysis of the effects of plastic surgery on face appearance and its challenges to face recognition. Then, to address FRAPS problem, an ensemble of Gabor Patch classifiers via Rank-Order list Fusion (GPROF) is proposed, inspired by the assumption of the interior consistency of face components in terms of identity. On the face database of plastic surgery, GPROF achieves much higher face identification rate than the best known results in the literature. Furthermore, with our impressive results, we suggest that plastic surgery detection should be paid more attend to. To address this problem, a partial matching based plastic surgery detection algorithm is proposed, aiming to detect four distinct types of surgery, i.e., the eyelid surgery, nose surgery, forehead surgery and face lift surgery. Our experimental results demonstrate that plastic surgery detection is a nontrivial task, and thus deserves more research efforts.

40 citations


Cites background from "Evolutionary granular approach for ..."

  • ...[9] proposed an evolutional granular approach....

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  • ...However, we must pointed out that the results of [9, 10] are from testing on 60% of the whole database, while those of [3, 11, 12] and ours are from testing on the whole database....

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References
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Book ChapterDOI
25 Jul 2001
TL;DR: The intent of the paper is to elaborate on the fundamentals of granular computing and put the entire area in a certain perspective while not moving into specific algorithmic details.
Abstract: The study is concerned with the fundamentals of granular computing. Granular computing, as the name itself stipulates, deals with representing information in the form of some aggregates (that embrace a number of individual entities) and their ensuing processing. We elaborate on the rationale behind granular computing. Next, a number of formal frameworks of information granulation are discussed including several alternatives such as fuzzy sets, interval analysis, rough sets, and probability. The notion of granularity itself is defined and quantified. A design agenda of granular computing is formulated and the key design problems are raised. A number of granular architectures are also discussed with an objective of delineating the fundamental algorithmic, and conceptual challenges. It is shown that the use of information granules of different size (granularity) lends itself to general pyramid architectures of information processing. The role of encoding and decoding mechanisms visible in this setting is also discussed in detail, along with some particular solutions. We raise an issue of interoperability of granular environments. The intent of the paper is to elaborate on the fundamentals and put the entire area in a certain perspective while not moving into specific algorithmic details.

710 citations

Journal ArticleDOI
01 Nov 2006
TL;DR: Findings from experimental studies of face recognition by humans provide insights into the nature of cues that the human visual system relies upon for achieving its impressive performance and serve as the building blocks for efforts to artificially emulate these abilities.
Abstract: A key goal of computer vision researchers is to create automated face recognition systems that can equal, and eventually surpass, human performance. To this end, it is imperative that computational researchers know of the key findings from experimental studies of face recognition by humans. These findings provide insights into the nature of cues that the human visual system relies upon for achieving its impressive performance and serve as the building blocks for efforts to artificially emulate these abilities. In this paper, we present what we believe are 19 basic results, with implications for the design of computational systems. Each result is described briefly and appropriate pointers are provided to permit an in-depth study of any particular result

699 citations


"Evolutionary granular approach for ..." refers background in this paper

  • ...established 19 results based on face recognition capabilities of the human mind [18]....

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  • ...edu and knowledge represented at different levels of information granularity [18]....

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BookDOI
01 Jan 2002
TL;DR: This book discusses Granular Computing in Data Mining, Granular computing with Closeness and Negligibility Relations, and the application of Granularity Computing to Confirm Compliance with Non-Proliferation Treaty.
Abstract: 1: Granular Computing - A New Paradigm.- Some Reflections on Information Granulation and its Centrality in Granular Computing, Computing with Words, the Computational Theory of Perceptions and Precisiated Natural Language.- 2: Granular Computing in Data Mining.- Data Mining Using Granular Computing: Fast Algorithms for Finding Association Rules.- Knowledge Discovery with Words Using Cartesian Granule Features: An Analysis for Classification Problems.- Validation of Concept Representation with Rule Induction and Linguistic Variables.- Granular Computing Using Information Tables.- A Query-Driven Interesting Rule Discovery Using Association and Spanning Operations.- 3: Data Mining.- An Interactive Visualization System for Mining Association Rules.- Algorithms for Mining System Audit Data.- Scoring and Ranking the Data Using Association Rules.- Finding Unexpected Patterns in Data.- Discovery of Approximate Knowledge in Medical Databases Based on Rough Set Model.- 4: Granular Computing.- Observability and the Case of Probability.- Granulation and Granularity via Conceptual Structures: A Perspective From the Point of View of Fuzzy Concept Lattices.- Granular Computing with Closeness and Negligibility Relations.- Application of Granularity Computing to Confirm Compliance with Non-Proliferation Treaty.- Basic Issues of Computing with Granular Probabilities.- Multi-dimensional Aggregation of Fuzzy Numbers Through the Extension Principle.- On Optimal Fuzzy Information Granulation.- Ordinal Decision Making with a Notion of Acceptable: Denoted Ordinal Scales.- A Framework for Building Intelligent Information-Processing Systems Based on Granular Factor Space.- 5: Rough Sets and Granular Computing.- GRS: A Generalized Rough Sets Model.- Structure of Upper and Lower Approximation Spaces of Infinite Sets.- Indexed Rough Approximations, A Polymodal System, and Generalized Possibility Measures.- Granularity, Multi-valued Logic, Bayes' Theorem and Rough Sets.- The Generic Rough Set Inductive Logic Programming (gRS-ILP) Model.- Possibilistic Data Analysis and Its Similarity to Rough Sets.

244 citations


"Evolutionary granular approach for ..." refers methods in this paper

  • ...To incorporate the above mentioned research findings, we propose a granular approach [5], [11] for facial feature extraction and matching....

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Journal ArticleDOI
TL;DR: The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance, so that future face recognition systems will be able to address this important problem.
Abstract: Advancement and affordability is leading to the popularity of plastic surgery procedures. Facial plastic surgery can be reconstructive to correct facial feature anomalies or cosmetic to improve the appearance. Both corrective as well as cosmetic surgeries alter the original facial information to a large extent thereby posing a great challenge for face recognition algorithms. The contribution of this research is 1) preparing a face database of 900 individuals for plastic surgery, and 2) providing an analytical and experimental underpinning of the effect of plastic surgery on face recognition algorithms. The results on the plastic surgery database suggest that it is an arduous research challenge and the current state-of-art face recognition algorithms are unable to provide acceptable levels of identification performance. Therefore, it is imperative to initiate a research effort so that future face recognition systems will be able to address this important problem.

187 citations


"Evolutionary granular approach for ..." refers background or methods in this paper

  • ...6% on the plastic surgery face database [17]....

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  • ...[17] describe several types of local and global plastic surgery procedures and their effect on different face recognition algorithms....

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  • ...To evaluate the performance of the proposed algorithm two different databases are used: plastic surgery face database [17] and heterogeneous non-plastic surgery database....

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  • ...In this research, publically available plastic surgery face database [17] is used....

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  • ...Plastic surgery has been recently established as a new and important covariate of face recognition alongside pose, expression, illumination, aging and disguise [17]....

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Proceedings ArticleDOI
01 Jan 2009
TL;DR: Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.
Abstract: We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated. Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized. A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail. The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.

124 citations


"Evolutionary granular approach for ..." refers background in this paper

  • ...2) Speeded Up Robust Features: SURF is a scale and rotation invariant descriptor [6], [9] that generates a compact representation of an image based on the spatial distribution of gradient information around the interest points....

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  • ...∙ Performance of SURF [6], [9], on the other hand, reduces when applied on face granules....

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