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 Jan 2018
TL;DR: A novel methodology for matching of illumination-invariant and heterogeneous faces is proposed, and a novel image representation called local extremum logarithm difference (LELD) is presented, whichoretical analysis proves that LELD is an illumination- Invariant edge feature in coarse level.
Abstract: A novel methodology for matching of illumination-invariant and heterogeneous faces is proposed here. We present a novel image representation called local extremum logarithm difference (LELD). Theoretical analysis proves that LELD is an illumination-invariant edge feature in coarse level. Since edges are invariant in different modalities, more importance is given on edges. Finally, a novel local zigzag binary pattern LZZBP is presented to capture the local variation of LELD, and we call it a zigzag pattern of local extremum logarithm difference (ZZPLELD). For refinement of ZZPLELD, a model based weight value learning is suggested. We tested the proposed methodology on different illumination variations, sketch-photo and NIR-VIS benchmark databases. Rank-1 recognition of 96.93% on CMU-PIE database and 95.81% on Extended Yale B database under varying illumination, show that ZZPLELD is an efficient method for illumination invariant face recognition. In the case of viewed sketches, the rank-1 recognition accuracy of 98.05% is achieved on CUFSF database. In the case of NIR-VIS matching, the rank-1 accuracy of 99.69% is achieved and which is superior to other state-of-the-art methods.
4 citations
Dissertation•
12 Dec 2016
TL;DR: A coordinated local metric learning (CLML) approach which learns local Mahalanobis metrics, and integrates them in a global representation where the l2 distance can be used, and shows that CLML improves over previous global and local metrics learning approaches for the task of face retrieval.
Abstract: In this dissertation, we propose methods and data driven machine learning solutions which address and benefit from the recent overwhelming growth of digital media content.First, we consider the problem of improving the efficiency of image retrieval. We propose a coordinated local metric learning (CLML) approach which learns local Mahalanobis metrics, and integrates them in a global representation where the l2 distance can be used. This allows for data visualization in a single view, and use of efficient ` 2 -based retrieval methods. Our approach can be interpreted as learning a linear projection on top of an explicit high-dimensional embedding of a kernel. This interpretation allows for the use of existing frameworks for Mahalanobis metric learning for learning local metrics in a coordinated manner. Our experiments show that CLML improves over previous global and local metric learning approaches for the task of face retrieval.Second, we present an approach to leverage the success of CNN models forvisible spectrum face recognition to improve heterogeneous face recognition, e.g., recognition of near-infrared images from visible spectrum training images. We explore different metric learning strategies over features from the intermediate layers of the networks, to reduce the discrepancies between the different modalities. In our experiments we found that the depth of the optimal features for a given modality, is positively correlated with the domain shift between the source domain (CNN training data) and the target domain. Experimental results show the that we can use CNNs trained on visible spectrum images to obtain results that improve over the state-of-the art for heterogeneous face recognition with near-infrared images and sketches.Third, we present convolutional neural fabrics for exploring the discrete andexponentially large CNN architecture space in an efficient and systematic manner. Instead of aiming to select a single optimal architecture, we propose a “fabric” that embeds an exponentially large number of architectures. The fabric consists of a 3D trellis that connects response maps at different layers, scales, and channels with a sparse homogeneous local connectivity pattern. The only hyperparameters of the fabric (the number of channels and layers) are not critical for performance. The acyclic nature of the fabric allows us to use backpropagation for learning. Learning can thus efficiently configure the fabric to implement each one of exponentially many architectures and, more generally, ensembles of all of them. While scaling linearly in terms of computation and memory requirements, the fabric leverages exponentially many chain-structured architectures in parallel by massively sharing weights between them. We present benchmark results competitive with the state of the art for image classification on MNIST and CIFAR10, and for semantic segmentation on the Part Labels dataset
4 citations
Posted Content•
TL;DR: A deep coupled framework to address the problem of matching sketch image against a gallery of mugshots and is able to make full use of the sketch and complementary facial attribute information to train a deep model compared to the conventional sketch-photo recognition methods.
Abstract: In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential in- formation about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a cou- pled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Con- volutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common em- bedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with differ- ent facial attributes, while the verification task reduces the intra-personal variations by pulling together all the fea- tures that are related to one person. The learned discrim- inative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary fa- cial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Exten- sive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the state- of-the-art models in sketch-photo recognition algorithms
3 citations
Cites methods from "Memetically Optimized MCWLD for Mat..."
...In the second approach, the discriminative methods utilize feature descriptors such as the scale-invariant feature transform (SIFT) [18], Weber’s local descriptor (WLD) [5], and multi-scale local binary pattern (MLBP) [11]....
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TL;DR: The motivation behind this paper is to present the effect of face tampering on various categories of face recognition algorithms and conclude that it is totally unpredictable to select particular type of algorithm for tampered face recognition.
Abstract: Modern face recognition systems are vulnerable to spoofing attack. Spoofing attack occurs when a person tries to cheat the system by presenting fake biometric data gaining unlawful access. A lot of researchers have originated novel techniques to fascinate these types of face tampering attack. It seems that no comparative studies of different face recognition algorithms on same protocols and fake data have been incorporated. The motivation behind this paper is to present the effect of face tampering on various categories of face recognition algorithms. For this purpose four categories of facial recognition algorithms have been selected to present the obtained results in the form of facial identification accuracy at various tampering and experimental protocols but obtained results are very fluctuating in nature. Finally, we come to the conclusion that it is totally unpredictable to select particular type of algorithm for tampered face recognition.
3 citations
24 Aug 2014
TL;DR: This work proposes the use of shape features for a preliminary selection of the candidate photos to be successively analyzed by more complex state-of-the-art techniques, and can be computed and matched in a very short time.
Abstract: Sketch recognition for forensic applications is a very challenging task and several solutions have been recently proposed. Considering that real mug shot databases can be very large, one important aspect to consider in this scenario is also the efficiency of the search procedure. This work proposes the use of shape features for a preliminary selection of the candidate photos to be successively analyzed by more complex state-of-the-art techniques. The proposed features can be computed and matched in a very short time, and at the same time they are able to significantly reduce the search space, thus allowing to speed up the recognition process.
3 citations
Cites background or methods from "Memetically Optimized MCWLD for Mat..."
...State of the art approaches can be classified on the basis of the technique used to tackle the previously mentioned modality-gap in two main categories: generative or discriminative [9]....
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...Unfortunately it is rather difficult to find public face datasets feasible in this scenario, so we created a mixed database of 8221 images taking well-controlled photos from various sources: 188 images from CUHK [4], 123 from AR [4], 65 from IIITD semi forensic [9], 114 from CVL [47], 100 from PUT [48], 1194 from Feret [6][7], 6387 from FRGC [49] and 50 real mug shots collected from the web....
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...Previously mentioned approaches [20], [9], [8] and [24] share the ability to deal with viewed sketches as well as forensic sketches (differences between them are exhaustively described in [8])....
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...Among the mentioned state of the art approaches, only a few works report results on so large datasets ([9][8][28])....
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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