Compared to a leading commercial face recognition system, LFDA offers substantial improvements in matching forensic sketches to the corresponding face images and leads to state-of-the-art accuracys when matching viewed sketches.
Abstract:
The problem of matching a forensic sketch to a gallery of mug shot images is addressed in this paper. Previous research in sketch matching only offered solutions to matching highly accurate sketches that were drawn while looking at the subject (viewed sketches). Forensic sketches differ from viewed sketches in that they are drawn by a police sketch artist using the description of the subject provided by an eyewitness. To identify forensic sketches, we present a framework called local feature-based discriminant analysis (LFDA). In LFDA, we individually represent both sketches and photos using SIFT feature descriptors and multiscale local binary patterns (MLBP). Multiple discriminant projections are then used on partitioned vectors of the feature-based representation for minimum distance matching. We apply this method to match a data set of 159 forensic sketches against a mug shot gallery containing 10,159 images. Compared to a leading commercial face recognition system, LFDA offers substantial improvements in matching forensic sketches to the corresponding face images. We were able to further improve the matching performance using race and gender information to reduce the target gallery size. Additional experiments demonstrate that the proposed framework leads to state-of-the-art accuracys when matching viewed sketches.
TL;DR: Zhang et al. as mentioned in this paper proposed a novel Orthogonal Modality Disentanglement and Representation Alignment (OMDRA) approach, which consists of three key components, including Modality-Invariant (MI) loss, orthogonal modality disentangler and deep representation alignment (DRA).
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.
TL;DR: Wang et al. as discussed by the authors proposed an attention-guided feature disentangling framework (AgFD) to eliminate the large cross-modality discrepancy for Heterogeneous Face Recognition (HFR).
TL;DR: Zhang et al. as mentioned in this paper use an intermediate latent space between the two modalities to match a given face sketch image against a face photo database, and employ a bidirectional (photo -> sketch and sketch -> photo) collaborative synthesis network.
TL;DR: Experiments conducted on HFB (VIS-NIR), Biosecure (Low-High or Webcam-Digitalcam) face databases validate the robustness and superiority over other methods inspired from Probabilistic Linear Discriminant Analysis.
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.
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.
TL;DR: It is observed that the ranking of the descriptors is mostly independent of the interest region detector and that the SIFT-based descriptors perform best and Moments and steerable filters show the best performance among the low dimensional descriptors.
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.
Q1. What contributions have the authors mentioned in the paper "Matching forensic sketches to mug shot photos" ?
The problem of matching a forensic sketch to a gallery of mug shot images is addressed in this paper. Forensic sketches differ from viewed sketches in that they are drawn by a police sketch artist using the description of the subject provided by an eyewitness. To identify forensic sketches, the authors present a framework called local feature-based discriminant analysis ( LFDA ). In LFDA, the authors individually represent both sketches and photos using SIFT feature descriptors and multiscale local binary patterns ( MLBP ). The authors were able to further improve the matching performance using race and gender information to reduce the target gallery size.
Q2. What is the proposed method for combining large feature size and small sample size?
In order to handle the combination of a large feature size and small sample size, an ensemble of linear discriminant classifiers called LFDA is proposed.
Q3. What other methods have been proposed to handle the SSS problem?
Other discriminant analysis methods have been proposed to handle the SSS problem, such as random sampling LDA [23], regularized LDA [24], and direct LDA [25].
Q4. Why are image descriptors not sufficiently verbose to describe a face?
Because most image descriptors are not sufficientlyverbose to fully describe a face image, the descriptors are computedover a set of uniformly distributed subregions of the face.
Q5. What is the main reason why the authors have not found a large number of forensic sketches?
the authors believe that with a larger number of forensic sketches, the authors could more properly train their discriminant and further improve the matching performance.
Q6. Why is the culprit being depicted in a forensic sketch considered a suspect?
This is because the culprit being depicted in a forensic sketch typically has committed a heinous crime (e.g., murder, rape, and armed robbery) that will receive a large amount of attention from investigators.
Q7. What is the approach to extract discriminant features?
A straightforward approach would be to apply classical subspace analysis (such as LDA) directly on , and to extract discriminant features for classification.
Q8. What are the key difficulties in matching forensic sketches?
The authors highlight two key difficulties in matching forensic sketches: 1) matching across image modalities and 2) performing face recognition despite possibly inaccurate depictions of the face.