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.
Citations
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TL;DR: An Identity-Aware CycleGAN (IACycleGAN) model is proposed that applies a new perceptual loss to supervise the image generation network and improves CycleGAN on photo-sketch synthesis by paying more attention to the synthesis of key facial regions, such as eyes and nose, which are important for identity recognition.
93 citations
TL;DR: The FaceSketchID System is presented, a scalable, and operationally deployable software system that achieves state-of-the-art matching accuracy on facial composites using two algorithms (holistic and component based); and a study of the effects of training data on algorithm performance is presented.
Abstract: Facial composites are widely used by law enforcement agencies to assist in the identification and apprehension of suspects involved in criminal activities. These composites, generated from witness descriptions, are posted in public places and media with the hope that some viewers will provide tips about the identity of the suspect. This method of identifying suspects is slow, tedious, and may not lead to the timely apprehension of a suspect. Hence, there is a need for a method that can automatically and efficiently match facial composites to large police mugshot databases. Because of this requirement, facial composite recognition is an important topic for biometrics researchers. While substantial progress has been made in nonforensic facial composite (or viewed composite) recognition over the past decade, very little work has been done using operational composites relevant to law enforcement agencies. Furthermore, no facial composite to mugshot matching systems have been documented that are readily deployable as standalone software. Thus, the contributions of this paper include: 1) an exploration of composite recognition use cases involving multiple forms of facial composites; 2) the FaceSketchID System, a scalable, and operationally deployable software system that achieves state-of-the-art matching accuracy on facial composites using two algorithms (holistic and component based); and 3) a study of the effects of training data on algorithm performance. We present experimental results using a large mugshot gallery that is representative of a law enforcement agency’s mugshot database. All results are compared against three state-of-the-art commercial-off-the-shelf face recognition systems.
82 citations
Cites background from "Memetically Optimized MCWLD for Mat..."
...For this type of composite, the time between observation and recall by a witness varies depending on the circumstances....
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...…in the literature, (v) evaluated the effect of the type of training data on the performance of our recognition system, and (vi) demonstrated that composites made from poor quality surveillance imagery can be used to identify the suspect that could not be identified based on the original image....
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19 May 2015
TL;DR: In the proposed algorithm, first the deep learning architecture based facial representation is learned using large face database of photos and then the representation is updated using small problem-specific training database.
Abstract: Sketch recognition is one of the integral components used by law enforcement agencies in solving crime. In recent past, software generated composite sketches are being preferred as they are more consistent and faster to construct than hand drawn sketches. Matching these composite sketches to face photographs is a complex task because the composite sketches are drawn based on the witness description and lack minute details which are present in photographs. This paper presents a novel algorithm for matching composite sketches with photographs using transfer learning with deep learning representation. In the proposed algorithm, first the deep learning architecture based facial representation is learned using large face database of photos and then the representation is updated using small problem-specific training database. Experiments are performed on the extended PRIP database and it is observed that the proposed algorithm outperforms recently proposed approach and a commercial face recognition system.
79 citations
Cites methods from "Memetically Optimized MCWLD for Mat..."
...Memeti- cally Optimized MCWLD for Matching Sketches With Digital Face Images....
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...Some notable research directions are: SIFT and MLBP based local feature-based discriminant analysis (LFDA) [13] and genetic optimization based Multiscale Circular Weber Local Descriptor (MCWLD) [8]....
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TL;DR: A novel cross-modality enumeration loss is proposed to eliminate the modality gap on local patch level, which is then integrated into a convolutional neural networks for deep local descriptor extraction.
72 citations
04 Jun 2013
TL;DR: The experimental results show that composite sketches are matched with higher accuracy than forensic sketches to the corresponding mugshots, and both of the face representations studied here yield higher sketch to photo matching accuracy compared to a commercial face matcher.
Abstract: Facial sketches are widely used by law enforcement agencies to assist in the identification and apprehension of suspects involved in criminal activities Sketches used in forensic investigations are either drawn by forensic artists (forensic sketches) or created with computer software (composite sketches) following the verbal description provided by an eyewitness or the victim These sketches are posted in public places and in media in hopes that some viewers will provide tips about the identity of the suspect This method of identifying suspects is slow and tedious and may not lead to apprehension of the suspect Hence, there is a need for a method that can automatically and quickly match facial sketches to large police mugshot databases We address the problem of automatic facial sketch to mugshot matching and, for the first time, compare the effectiveness of forensic sketches and composite sketches The contributions of this paper include: (i) a database containing mugshots and corresponding forensic and composite sketches that will be made available to interested researchers; (ii) a comparison of holistic facial representations versus component based representations for sketch to mugshot matching; and (iii) an analysis of the effect of filtering a mugshot gallery using three sources of demographic information (age, gender and race/ethnicity) Our experimental results show that composite sketches are matched with higher accuracy than forensic sketches to the corresponding mugshots Both of the face representations studied here yield higher sketch to photo matching accuracy compared to a commercial face matcher
71 citations
Additional excerpts
...[18] Multi-scale circular Weber’s local descriptor...
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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