Evolutionary granular approach for recognizing faces altered due to plastic surgery
21 Mar 2011-pp 720-725
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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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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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
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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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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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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TL;DR: This paper showed that the inner face advantage is a developmental rather than a maturational phenomenon and discussed the implications of the failure to show a qualitatively adult-like pattern of face recognition before adolescence.
Abstract: Known faces are recognized better from their inner than outer parts (Ellis, Shepherd, & Davies, 1979). This has previously been demonstrated with cropped images. Using a blurring technique to defocus different parts of the face image systematically, we confirmed the effect for adults viewing famous faces (Experiment1). Children aged 5–13 years showed an outer-face advantage (Experiments 2 and 3). The inner-face advantage was found only at 15 years (Experiment 3). Experiment 4 showed an outer-face advantage in familiar face recognition when the viewers were adolescents with a mental age of under 10 years. The emergence of the inner-face advantage is a developmental rather than a maturational phenomenon. We discuss the implications of the failure to show a qualitatively adult-like pattern of face recognition before adolescence in relation to theories and models of face recognition.
92 citations
"Evolutionary granular approach for ..." refers background in this paper
...reported that different inner and outer facial regions represent distinct information which is helpful for face recognition [8]....
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TL;DR: A multi-stage system for single face tracking in cluttered scenes is presented, which includes a novel ratio-ratios operator that improves recognition rates by examining higher order relationships within the initial ratio template measures, and simple morphological eye and mouth feature detection.
35 citations
"Evolutionary granular approach for ..." refers background or methods in this paper
...Given the eye coordinates, 16 local facial fragments are extracted using the golden ratio face template [4] shown in Fig....
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...(a) Golden ratio face template [4], (b) face granules from third level of granularity....
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