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.
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.
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01 Jul 2017
TL;DR: Experimental evaluation and analysis on the proposed dataset show the effectiveness of the transfer learning approach for performing cross-modality recognition.
Abstract: Matching facial sketches to digital face images has widespread application in law enforcement scenarios. Recent advancements in technology have led to the availability of sketch generation tools, minimizing the requirement of a sketch artist. While these sketches have helped in manual authentication, matching composite sketches with digital mugshot photos automatically show high modality gap. This research aims to address the task of matching a composite face sketch image to digital images by proposing a transfer learning based evolutionary algorithm. A new feature descriptor, Histogram of Image Moments, has also been presented for encoding features across modalities. Moreover, IIITD Composite Face Sketch Database of 150 subjects is presented to fill the gap due to limited availability of databases in this problem domain. Experimental evaluation and analysis on the proposed dataset show the effectiveness of the transfer learning approach for performing cross-modality recognition.
24 citations
Cites background or methods from "Memetically Optimized MCWLD for Mat..."
...Forensic Sketch Database: It comprises of 190 forensic sketch-digital image pairs from the IIIT-Delhi Sketch Database [5] which were obtained from works by Lois Gibson [10], Karen Taylor [27] and few sources from Internet....
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...Viewed Sketch Database: It comprises 482 sketch-digital image pairs from the IIIT-Delhi Viewed Sketch Database [5]....
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...Semi-Forensic Database: It consists of 106 semi-forensic sketch-digital image pairs from the IIIT-Delhi Sketch Database [5]....
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...Experiment 4 - Forensic Hand-Drawn Sketches: The source dataset consists of 190 pairs of forensic sketches and digital face images from the IIIT-Delhi Sketch Database [5]....
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...[5] 2012 Genetic optimization based Multiscale Circular Weber Local Descriptor (MCWLD) Hand-Drawn Sketches (Generative Approaches)...
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TL;DR: This paper proposes a novel sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned face recognition in heterogeneous scenarios, and results illustrate that this approach achieves superior performance in comparison with state-of-the-art methods.
23 citations
Cites background or methods from "Memetically Optimized MCWLD for Mat..."
...In order to mimic the gap between viewed sketch recognition and forensic sketch recognition, [26] proposed a semi-forensic sketch dataset and deployed the multi-scale circular Weber’s local descriptor (MCWLD) for matching....
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...It is observed that classifiers trained on semi-forensic sketches can better fit forensic sketch recognition scenario [26]....
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...The modality invariant feature descriptor based methods [1], [3], [21], [22], [23], [24], [25], [26], [27], [28], [29] first represent face images by extracting modality invariant features which are then measured for matching....
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...The MCWLD method [26] utilized 6324 photos to extend the gallery and achieved a rank-50 accuracy of 28....
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TL;DR: This paper defines a set of qualitative face features both for caricatures and photographs where features are automatically extracted from photos and manually labeled in caricatures to solve the cross-modal face matching problem.
Abstract: Facial caricatures are informative funny images that allow us to identify a subject even with a few lines and dots. Matching caricatures to photographs is a challenging cross-modal face matching problem. This paper addresses this problem by defining a set of qualitative face features both for caricatures and photographs where features are automatically extracted from photos and manually labeled in caricatures. Additionally we release a publicly available caricature-photograph database with 200 caricatures and corresponding photomates. In our experiments, we use genetic algorithms and logistic regression and achieve over $$\mathrm{30.0}\,\%$$
recognition rate at 0.1 false accept rate.
23 citations
TL;DR: A novel methodology for heterogeneous face recognition, such as sketch-photo and near infrared (NIR)-visible (VIS) images is proposed, and a robust local image representation called local maximum quotient (LMQ) for capturing modality-invariant facial features is presented.
22 citations
Posted Content•
TL;DR: Wang et al. as mentioned in this paper proposed a sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned face recognition in heterogeneous scenarios, where a Markov networks model is constructed to generate adaptive sparse vectors.
Abstract: Face images captured in heterogeneous environments, e.g., sketches generated by the artists or composite-generation software, photos taken by common cameras and infrared images captured by corresponding infrared imaging devices, usually subject to large texture (i.e., style) differences. This results in heavily degraded performance of conventional face recognition methods in comparison with the performance on images captured in homogeneous environments. In this paper, we propose a novel sparse graphical representation based discriminant analysis (SGR-DA) approach to address aforementioned face recognition in heterogeneous scenarios. An adaptive sparse graphical representation scheme is designed to represent heterogeneous face images, where a Markov networks model is constructed to generate adaptive sparse vectors. To handle the complex facial structure and further improve the discriminability, a spatial partition-based discriminant analysis framework is presented to refine the adaptive sparse vectors for face matching. We conducted experiments on six commonly used heterogeneous face datasets and experimental results illustrate that our proposed SGR-DA approach achieves superior performance in comparison with state-of-the-art methods.
22 citations
References
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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....
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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
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