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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TL;DR: A new 3-D system able to automatically suggest, for selected facial features as nose, chin, etc., shapes that aesthetically match the patient's face, is presented.
Abstract: Face plastic surgery (PS) plays a major role in today medicine. Both for reconstructive and cosmetic surgery, achieving harmony of facial features is an important, if not the major goal. Several systems have been proposed for presenting to patient and surgeon possible outcomes of the surgical procedure. In this paper, we present a new 3-D system able to automatically suggest, for selected facial features as nose, chin, etc., shapes that aesthetically match the patient's face. The basic idea is suggesting shape changes aimed to approach similar but more harmonious faces. To this goal, our system compares the 3-D scan of the patient with a database of scans of harmonious faces, excluding the feature to be corrected. Then, the corresponding features of the k most similar harmonious faces, as well as their average, are suitably pasted onto the patient's face, producing k+1 aesthetically effective surgery simulations. The system has been fully implemented and tested. To demonstrate the system, a 3-D database of harmonious faces has been collected and a number of PS treatments have been simulated. The ratings of the outcomes of the simulations, provided by panels of human judges, show that the system and the underlying idea are effective.
26 citations
01 Sep 2013
TL;DR: The proposed framework, a S-SIM index weighted multi-patch fusion scheme is developed, where different weights are provided to different patches in accordance with the degree to which each patch may be altered by surgeries, achieving performance comparable with the current state-of-the-art.
Abstract: Variations in the face appearance caused by plastic surgery on skin texture and geometric structure, can impair the performance of most current face recognition systems. In this work, we proposed to use the Structural Similarity (SSIM) quality map to detect and model variations due to plastic surgeries. In the proposed framework, a S-SIM index weighted multi-patch fusion scheme is developed, where different weights are provided to different patches in accordance with the degree to which each patch may be altered by surgeries. An important feature of the proposed approach, also achieving performance comparable with the current state-of-the-art, is that neither training process is needed nor any background information from other datasets is required. Extensive experiments conducted on a plastic surgery face database demonstrate the potential of SSIM map for matching face images after surgeries.
22 citations
Cites background from "Evolutionary granular approach for ..."
...proposed an evolutionary granular approach to extract discriminative information from non-disjoint face granules [3]....
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TL;DR: This paper presents a multiple projective dictionary learning (MPDL) framework that does not require the computation of I0 and I1 norms, and integrates the proposed MPDL framework for face verification with two commercial systems to demonstrate an improvement in verification performance on a combined database of plastic surgery and regular face images.
Abstract: Researchers have shown that the changes in face features due to plastic surgery can be modeled as a covariate that reduces the ability of algorithms to recognize a person’s identity. Traditional dictionary learning methods learn a sparse representation using $l_{0}$ and $l_{1}$ norms that are computationally expensive. This paper presents a multiple projective dictionary learning (MPDL) framework that does not require the computation of $l_{0}$ and $l_{1}$ norms. We propose a novel solution to discriminate plastic surgery faces from regular faces by learning representations of local and global plastic surgery faces using multiple projective dictionaries and by using compact binary face descriptors. Experimental results on the plastic surgery database show that the proposed MPDL framework is able to detect plastic surgery faces with a high accuracy of 97.96%. To verify the identity of a person, the detected plastic surgery faces are divided into local regions of interest (ROIs) that are likely to be altered by a particular plastic surgery. The cosine distance between the compact binary face descriptors is computed for each ROI in the detected plastic surgery faces. In addition, we compute the human visual system feature similarity score based on phase congruency and gradient magnitude between the same ROIs. The cosine distance scores and the feature similarity scores are combined to learn a support vector machine model to verify if the faces belong to the same person. We integrate our proposed MPDL framework for face verification with two commercial systems to demonstrate an improvement in verification performance on a combined database of plastic surgery and regular face images.
16 citations
Cites methods from "Evolutionary granular approach for ..."
...[6] proposed an evolutionary algorithm to compute optimal weights for face granules during the face matching process....
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TL;DR: The performance of the proposed facial recognition system surpassed the performance of a number of state-of-the-art face recognition after plastic surgery, with a maximum verification accuracy of more than 91%.
Abstract: Plastic surgery is considered as a challenging research issue in the field of face recognition. Nevertheless, it has yet to be studied from theoretical and experimental perspectives. In this study, the authors proposed a facial recognition system for recognising faces after plastic surgery, which fuses the scores of two feature-based and texture-based algorithms. The feature based algorithm is the image GIST global descriptor and the texture-based algorithm is the local binary pattern (LBP) of silence points. First, the local texture descriptor LBP was applied over a set of key points (silence points) in the face image rather than applying it over the entire face area. This proposed feature set is based on the assumption that only those LBP patterns with certain meaning, such as an edge or corner, will be useful for recognising faces that have undergone plastic surgery. The second set of features was extracted using a global descriptor, which is the GIST descriptor, to obtain a basic and a subordinate level description of the perceptual dimension. The performance of the proposed system surpassed the performance of a number of state-of-the-art face recognition after plastic surgery, with a maximum verification accuracy of more than 91%.
15 citations
Cites methods from "Evolutionary granular approach for ..."
...[9] adopted an evolutionary three-level granular approach with speeded up robust features (SURF) and CLBP features towards processing tessellated facial images....
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TL;DR: Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.
Abstract: Plastic surgery procedures on the face introduce skin texture variations between images of the same person (intra-subject), thereby making the task of face recognition more difficult than in normal scenario. Usually, in contemporary face recognition systems, the original gray-level face image is used as input to the Gabor descriptor, which translates to encoding some texture properties of the face image. The texture-encoding process significantly degrades the performance of such systems in the case of plastic surgery due to the presence of surgically induced intra-subject variations. Based on the proposition that the shape of significant facial components such as eyes, nose, eyebrow, and mouth remains unchanged after plastic surgery, this paper employs an edge-based Gabor feature representation approach for the recognition of surgically altered face images. We use the edge information, which is dependent on the shapes of the significant facial components, to address the plastic surgery-induced texture variation problems. To ensure that the significant facial components represent useful edge information with little or no false edges, a simple illumination normalization technique is proposed for preprocessing. Gabor wavelet is applied to the edge image to accentuate on the uniqueness of the significant facial components for discriminating among different subjects. The performance of the proposed method is evaluated on the Georgia Tech (GT) and the Labeled Faces in the Wild (LFW) databases with illumination and expression problems, and the plastic surgery database with texture changes. Results show that the proposed edge-based Gabor feature representation approach is robust against plastic surgery-induced face variations amidst expression and illumination problems and outperforms the existing plastic surgery face recognition methods reported in the literature.
12 citations
Cites background or methods from "Evolutionary granular approach for ..."
...[24,25], global surgeries like rhytidectomy severely impacted on their recognition algorithm....
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...Face recognition performance in plastic surgery scenarios for cases such as rhytidectomy (face and mid-face lift), rhinoplasty (nose reshaping), blepharoplasty (eye surgery), otoplasty (ear surgery), browlift, dermabrasion, and skin peeling has been investigated [2,22-24,35-38]....
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...[24,25] adopted non-disjoint face granulation approach where the granules are obtained from HE-normalized images....
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...In other words, rhytidectomy is an embodiment of local and global appearance-changing surgery procedures, which may explain the challenges that the existing intensity-based recognition methods in the case of rhytidectomy [2] and subsequent works [22-25] faced....
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References
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Book•
01 Sep 1988
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Abstract: From the Publisher:
This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields
Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required
52,797 citations
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
Abstract: Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.
14,245 citations
"Evolutionary granular approach for ..." refers background or methods in this paper
...CLBP can be further extended to Uniform CLBP [13] to achieve robustness to rotation variations and dimensionality reduction....
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...Further, two feature extractors, namely Uniform Circular Local Binary Pattern (UCLBP) [13] and Speeded Up Robust Features (SURF) [6], are used for extracting discriminating information from face granules....
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...Circular local binary pattern (CLBP) [3], [13] is a feature extractor where the texture descriptor is computed based on the neighboring pixels that are well separated on a circle around the central pixel....
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...1) Uniform Circular Local Binary Patterns: Local binary pattern based descriptor [3], [13] is a widely used texture operator because of its robustness to gray level changes and high computational efficiency....
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...Uniform CLBP is described using Equations 5 and 6....
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07 May 2006
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Abstract: In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.
13,011 citations
"Evolutionary granular approach for ..." refers background or methods in this paper
...Further, two feature extractors, namely Uniform Circular Local Binary Pattern (UCLBP) [13] and Speeded Up Robust Features (SURF) [6], are used for extracting discriminating information from face granules....
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...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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TL;DR: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features that is assessed in the face recognition problem under different challenges.
Abstract: This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed
5,563 citations
"Evolutionary granular approach for ..." refers background or methods in this paper
...1) Uniform Circular Local Binary Patterns: Local binary pattern based descriptor [3], [13] is a widely used texture operator because of its robustness to gray level changes and high computational efficiency....
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...2) For assigning weights to each face granule, a chromosome with weights proportional to the identification accuracy of each face granule (as proposed by Ahonen [3]) is used as the initial chromosome....
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...Circular local binary pattern (CLBP) [3], [13] is a feature extractor where the texture descriptor is computed based on the neighboring pixels that are well separated on a circle around the central pixel....
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TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Abstract: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.
4,816 citations
"Evolutionary granular approach for ..." refers methods in this paper
...from the CMU-PIE database [16], 661 subjects from the FERET database [14], 36 subjects from the Georgia Tech database [1] and 23 subjects from the GTAV database [2]....
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