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Journal ArticleDOI

Memetically Optimized MCWLD for Matching Sketches With Digital Face Images

01 Oct 2012-IEEE Transactions on Information Forensics and Security (IEEE)-Vol. 7, Iss: 5, pp 1522-1535

TL;DR: An automated algorithm to extract discriminating information from local regions of both sketches and digital face images is presented and yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.

AbstractOne of the important cues in solving crimes and apprehending criminals is matching sketches with digital face images. This paper presents an automated algorithm to extract discriminating information from local regions of both sketches and digital face images. Structural information along with minute details present in local facial regions are encoded using multiscale circular Weber's local descriptor. Further, an evolutionary memetic optimization algorithm is proposed to assign optimal weight to every local facial region to boost the identification performance. Since forensic sketches or digital face images can be of poor quality, a preprocessing technique is used to enhance the quality of images and improve the identification performance. Comprehensive experimental evaluation on different sketch databases show that the proposed algorithm yields better identification performance compared to existing face recognition algorithms and two commercial face recognition systems.

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Citations
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Journal ArticleDOI
Abstract: Heterogeneous face recognition (HFR) refers to matching face images acquired from different sources (i.e., different sensors or different wavelengths) for identification. HFR plays an important role in both biometrics research and industry. In spite of promising progresses achieved in recent years, HFR is still a challenging problem due to the difficulty to represent two heterogeneous images in a homogeneous manner. Existing HFR methods either represent an image ignoring the spatial information, or rely on a transformation procedure which complicates the recognition task. Considering these problems, we propose a novel graphical representation based HFR method (G-HFR) in this paper. Markov networks are employed to represent heterogeneous image patches separately, which takes the spatial compatibility between neighboring image patches into consideration. A coupled representation similarity metric (CRSM) is designed to measure the similarity between obtained graphical representations. Extensive experiments conducted on multiple HFR scenarios (viewed sketch, forensic sketch, near infrared image, and thermal infrared image) show that the proposed method outperforms state-of-the-art methods.

104 citations

Journal ArticleDOI
TL;DR: A novel multiple representations-based face sketch-photo-synthesis method that adaptively combines multiple representations to represent an image patch that combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches.
Abstract: Face sketch–photo synthesis plays an important role in law enforcement and digital entertainment. Most of the existing methods only use pixel intensities as the feature. Since face images can be described using features from multiple aspects, this paper presents a novel multiple representations-based face sketch–photo-synthesis method that adaptively combines multiple representations to represent an image patch. In particular, it combines multiple features from face images processed using multiple filters and deploys Markov networks to exploit the interacting relationships between the neighboring image patches. The proposed framework could be solved using an alternating optimization strategy and it normally converges in only five outer iterations in the experiments. Our experimental results on the Chinese University of Hong Kong (CUHK) face sketch database, celebrity photos, CUHK Face Sketch FERET Database, IIIT-D Viewed Sketch Database, and forensic sketches demonstrate the effectiveness of our method for face sketch–photo synthesis. In addition, cross-database and database-dependent style-synthesis evaluations demonstrate the generalizability of this novel method and suggest promising solutions for face identification in forensic science.

99 citations


Cites methods from "Memetically Optimized MCWLD for Mat..."

  • ...Thus, we utilize RS-LDA as the classifier and compared the proposed strategy with three recent methods [17], [56], [57]....

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  • ...The same protocol, as in [56], was followed, and our method achieves a rank-50 accuracy of 37....

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  • ...The method in [56] utilized modified Weber’s local descriptor and memetic optimization....

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Journal ArticleDOI
TL;DR: This survey provides a comprehensive review of established techniques and recent developments in HFR, and offers a detailed account of datasets and benchmarks commonly used for evaluation.
Abstract: Heterogeneous face recognition (HFR) refers to matching face imagery across different domains It has received much interest from the research community as a result of its profound implications in law enforcement A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years This survey provides a comprehensive review of established techniques and recent developments in HFR Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation We finish by assessing the state of the field and discussing promising directions for future research Display Omitted Provide a comprehensive review of established techniques in HFRProvide a thorough review of recent developments in HFROffer a detailed account of datasets and benchmarks commonly used for evaluationAssess the state of the field and discuss promising directions for future research

98 citations


Cites background from "Memetically Optimized MCWLD for Mat..."

  • ...These mid-level facial features were manually annotated for each image, and used together with automatically extracted LBP [112] features....

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  • ...Feature design strategies [29, 30, 31, 32] focus on engineering or learning features that are invariant to the modalities in question, while simultaneously being discriminative for person identity....

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  • ...Cross-domain Strategies Feature-centric crossdomain strategies [29, 30, 31, 32, 11, 27, 34, 33, 21, 39] can be seen as designing improved feature extractors...

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  • ...The most widely used image feature descriptors are Scale-invariant feature transform (SIFT), Gabor transform, Histogram of Averaged Oriented Gradients (HAOG) and Local Binary Pattern (LBP)....

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  • ...Multi-class (Tr) Bayesian [8], Metric learning [32] Metric learning [53] SVM [48]...

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Book ChapterDOI
08 Oct 2016
TL;DR: Experimental results show that CNNs trained on visible spectrum images can be used to obtain results that are on par or improve over the state-of-the-art for heterogeneous recognition with near-infrared images and sketches.
Abstract: Heterogeneous face recognition aims to recognize faces across different sensor modalities. Typically, gallery images are normal visible spectrum images, and probe images are infrared images or sketches. Recently significant improvements in visible spectrum face recognition have been obtained by CNNs learned from very large training datasets. In this paper, we are interested in the question to what extent the features from a CNN pre-trained on visible spectrum face images can be used to perform heterogeneous face recognition. We explore different metric learning strategies to reduce the discrepancies between the different modalities. Experimental results show that we can use CNNs trained on visible spectrum images to obtain results that are on par or improve over the state-of-the-art for heterogeneous recognition with near-infrared images and sketches.

91 citations

Journal ArticleDOI
16 Jul 2014-PLOS ONE
TL;DR: An automated algorithm is developed to verify the faces presented under disguise variations using automatically localized feature descriptors which can identify disguised face patches and account for this information to achieve improved matching accuracy.
Abstract: Face verification, though an easy task for humans, is a long-standing open research area. This is largely due to the challenging covariates, such as disguise and aging, which make it very hard to accurately verify the identity of a person. This paper investigates human and machine performance for recognizing/verifying disguised faces. Performance is also evaluated under familiarity and match/mismatch with the ethnicity of observers. The findings of this study are used to develop an automated algorithm to verify the faces presented under disguise variations. We use automatically localized feature descriptors which can identify disguised face patches and account for this information to achieve improved matching accuracy. The performance of the proposed algorithm is evaluated on the IIIT-Delhi Disguise database that contains images pertaining to 75 subjects with different kinds of disguise variations. The experiments suggest that the proposed algorithm can outperform a popular commercial system and evaluates them against humans in matching disguised face images.

90 citations


Cites methods from "Memetically Optimized MCWLD for Mat..."

  • ...Earlier research has primarily focused on the challenges or covariates of pose, illumination and expression whereas recently, face alterations due to plastic surgery [9], sketch-to-photo matching [10,11], multi-spectrum matching [12–14], aging [15–17], and disguise [18–20] are also being explored....

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References
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Journal ArticleDOI
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Abstract: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

42,225 citations

01 Jan 2011
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Abstract: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.

14,701 citations


Additional excerpts

  • ...On the other hand, sparse descriptor such as Scale Invariant Feature Transform (SIFT ) [23] is based on interest point detection and computing the descriptor in the vicinity of detected interest points....

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Journal ArticleDOI
Abstract: Introduction Preliminaries and notation The what, why, and how of wavelets The continuous wavelet transform Discrete wavelet transforms: Frames Time-frequency density and orthonormal bases Orthonormal bases of wavelets and multiresolutional analysis Orthonormal bases of compactly supported wavelets More about the regularity of compactly supported wavelets Symmetry for compactly supported wavelet bases Characterization of functional spaces by means of wavelets Generalizations and tricks for orthonormal wavelet bases References Indexes.

14,139 citations

Journal ArticleDOI
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

5,237 citations

01 Jan 1998

3,550 citations