scispace - formally typeset
Search or ask a question
Journal ArticleDOI

Memetically Optimized MCWLD for Matching Sketches With Digital Face Images

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.
Abstract: One 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.
Citations
More filters
Posted Content
02 Jun 2017
TL;DR: This paper exploits the potential of deep learning networks in filling large missing region and generating realistic faces with high-fidelity in cross domains and proposes the recursive generation by bidirectional transformation networks (rBTN) that recursively generates a whole face/sketch from a small sketch/face patch.
Abstract: We start by asking an interesting yet challenging question, “If a large proportion (e.g., more than 90% as shown in Fig. 1) of the face/sketch is missing, can a realistic whole face sketch/image still be estimated?” Existing face completion and generation methods either do not conduct domain transfer learning or can not handle large missing area. For example, the inpainting approach tends to blur the generated region when the missing area is large (i.e., more than 50%). In this paper, we exploit the potential of deep learning networks in filling large missing region (e.g., as high as 95% missing) and generating realistic faces with high-fidelity in cross domains. We propose the recursive generation by bidirectional transformation networks (rBTN) that recursively generates a whole face/sketch from a small sketch/face patch. The large missing area and the cross domain challenge make it difficult to generate satisfactory results using a unidirectional cross-domain learning structure. On the other hand, a forward and backward bidirectional learning between the face and sketch domains would enable recursive estimation of the missing region in an incremental manner (Fig. 1) and yield appealing results. r-BTN also adopts an adversarial constraint to encourage the generation of realistic faces/sketches. Extensive experiments have been conducted to demonstrate the superior performance from r-BTN as compared to existing potential solutions.

7 citations


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

  • ...We collect 1,577 face/sketch pairs from the datasets CUHK [24], CUFSF [28], AR [13], FERET [17], and IIITD [1]....

    [...]

Dissertation
01 Jul 2017
TL;DR: This dissertation addresses the challenge of heterogeneous face matching scenarios, such as matching a sketch against a mugshot dataset of digital photographs, cross-spectrum, and crossresolution matching, that arise in a wide range of law enforcement scenarios, and develops an approach to efficiently update the face recognition engine to incorporate incremental training data.
Abstract: Due to the unconstrained nature of data capture and non-cooperative subjects, automatic face recognition is still a research challenge for application scenarios such as law enforcement. We observe that challenges of face recognition are broadly rooted into two facets: (1) the non-ideal and possibly adversarial face image samples and (2) the large size and incremental/streaming availability of data. The first facet encompasses various challenges such as intentional or unintentional obfuscation of identity, attempts for spoofing system, user non-cooperation, and large intra-subject variations for heterogeneous face recognition. The second facet caters to challenges arising due to application scenarios such as repeat offender identification and surveillance where the data is either large scale or available incrementally. Along with advancing the face recognition research by addressing the challenges arising from both the aforementioned facets, this dissertation also contributes to the pattern classification research by abstracting the research problems at the classifier level and proposing feature independent solutions to some of the problems. The first contribution addresses the challenge of face obfuscation due to usage of disguise accessories. We collect and benchmark IIIT In and Beyond Visible Spectrum Face Dataset (IBVSD) pertaining to 75 subjects, which has various types of disguises applied on different individuals. It has become one of the most used disguise face dataset in the research community. Since disguised facial regions can lead to erroneous identity prediction, a texture based algorithm is designed to differentiate between biometric and non-biometric facial patches. The proposed approach is embedded with local face recognition algorithm to address the challenge of disguise variations. The approach is further enhanced with the use of thermal spectrum imaging. As the second contribution, the dissertation addresses the challenge of heterogeneous face matching scenarios, such as matching a sketch against a mugshot dataset of digital photographs, cross-spectrum, and crossresolution matching, that arise in a wide range of law enforcement scenarios. Heterogeneous Discriminant Analysis (HDA) is designed to encode multi-view heterogeneity in the classifier to obtain a projection space more suitable for matching. Further, to extend the proposed technique for nonlinear projections, formulation of kernel HDA is proposed. Focusing on application such as identification of repeat offenders, as the third contribution, we develop an approach to efficiently update the face recognition engine to incorporate incremental training data. The proposed Incremental Semi-Supervised Discriminant Analysis (ISSDA) provides mechanism to efficiently, in terms of accuracy and training time, update the discriminatory projection directions. The proposed approach capitalizes on offline unlabeled face image data, which is inexpensive to obtain and generally available in abundance. The fourth contribution of this dissertation is focused on designing

6 citations


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

  • ...While there is some research on forensic hand-drawn sketches [179], [180], the research pertaining to composite sketch matching is relatively less explored....

    [...]

Journal ArticleDOI
TL;DR: Using WLD, the different challenges of image analysis and recognition features with respect to illumination changes, contrast differences, and geometrical transformations like rotation, scaling, translation, and mirroring are addressed.
Abstract: Weber local descriptor (WLD) is applied for addressing the challenges in image/pattern problems, especially in computer vision and pattern recognition domains. In this paper, we review literature on theories and applications of WLD. Using WLD, we address the different challenges of image analysis and recognition features with respect to illumination changes, contrast differences, and geometrical transformations like rotation, scaling, translation, and mirroring. Further, the role of the classifiers and experimental protocols used in the different applications are discussed. Applications include texture classification, medical imaging, agricultural safety, fingerprint analysis, forgery analysis, and face recognition.

6 citations

Proceedings ArticleDOI
06 Jul 2014
TL;DR: This paper investigates the application of a novel Deep Neural Network architecture to the problem of matching data in different modes and the results compared to traditional approaches employing Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA).
Abstract: This paper investigates the application of a novel Deep Neural Network (DNN) architecture to the problem of matching data in different modes. Initially one DNN is pre-trained as a feature extractor using several stacked Restricted Boltzmann Machine (RBM) blocks on the entire training data using unsupervised learning. This DNN is duplicated and each net is fine-tuned by training on the data represented in a specific mode using supervised learning. The target of each DNN is linked to the output from the other DNN thus ensuring matching features are learnt which are adjusted to take differing representation into account. These features are used with some distance metric to determine matches. The expected benefit of this approach is utilizing the capability of DNN to learn higher level features which can better capture the information contained in the input data's structure, while ensuring the differences in data representation are accounted for. The architecture is applied to the problem of matching faces and sketches and the results compared to traditional approaches employing Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA).

6 citations


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

  • ...This problem has been recently investigated by a number of researchers e.g. [11], [12], [13], [14] and it is an important real-life law enforcement application....

    [...]

Journal ArticleDOI
TL;DR: A new technology to match composite sketches with images captured by unmanned aerial vehicle to apprehend first-time criminals in a very short time period is developed.
Abstract: Police database cannot have images of first-time offenders; hence, apprehending them becomes a very challenging task. In this paper, we propose a novel technique to apprehend first-time offenders using composite sketches and images captured by unmanned aerial vehicles. The key contribution of this paper is we have developed a new technology to match composite sketches with images captured by unmanned aerial vehicle to apprehend first-time criminals in a very short time period. The unmanned aerial vehicle is sent in the area where the first-time offender is likely to be present. The image captured by unmanned aerial vehicle is passed to face detection module so that only human faces are obtained. Feature extraction is performed using multi-resolution uniform local binary pattern, and classification is performed using dictionary matching. This proposed method is validated by composite sketches generated using SketchCop FACETTE face design system software and images captured by Phantom 3 professional unmanned aerial vehicle.

6 citations

References
More filters
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.

46,906 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.

14,708 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....

    [...]

Journal ArticleDOI
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
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.

14,157 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

5,563 citations

01 Jan 1998

3,650 citations