A Large-Scale Software-Generated Face Composite Sketch Database

Galea, Christian; Farrugia, Reuben A.

doi:10.1109/biosig.2016.7736902

Cited by 21 publications

(24 citation statements)

References 19 publications

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“…We evaluate our approach on two tasks in sketch datasets: photograph to sketch recognition and sketch to photograph recognition on CUFS dataset [30,47], CUFSF dataset [47,52], IIIT-D Sketch dataset [2], PRIP-VSGC dataset [3,18], PRIP-HDC dataset [23], MGDB dataset [35], UoM-SGFS dataset [16], and VIPSL dataset [37] respectively. The photograph to sketch recognition here is: given real faces of a public figure, we can recognize all the sketch faces of that public figure from a dataset of the sketch.…”

Section: Sketch Face Recognitionmentioning

confidence: 99%

Learning from the Past: Meta-Continual Learning with Knowledge Embedding for Jointly Sketch, Cartoon, and Caricature Face Recognition

Zheng

Lan

Wang

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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This paper deals with a challenging task of learning from different modalities by tackling the difficulty problem of jointly face recognition between abstract-like sketches, cartoons, caricatures and real-life photographs. Due to the significant variations in the abstract faces, building vision models for recognizing data from these modalities is an extremely challenging. We propose a novel framework termed as Meta-Continual Learning with Knowledge Embedding to address the task of jointly sketch, cartoon, and caricature face recognition. In particular, we firstly present a deep relational network to capture and memorize the relation among different samples. Secondly, we present the construction of our knowledge graph that relates image with the label as the guidance of our meta-learner. We then design a knowledge embedding mechanism to incorporate the knowledge representation into our network. Thirdly, to mitigate catastrophic forgetting, we use a meta-continual model that updates our ensemble model and improves its prediction accuracy. With this meta-continual model, our network can learn from its past. The final classification is derived from our network by learning to compare the features of samples.

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Section: Sketch Face Recognitionmentioning

confidence: 99%

Learning from the Past: Meta-Continual Learning with Knowledge Embedding for Jointly Sketch, Cartoon, and Caricature Face Recognition

Zheng

Lan

Wang

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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show abstract

“…The work done in is most applicable to this letter, yet the quantity of subjects and portrays utilized was restricted since the last were utilized physically made by utilizing a few craftsmen or programming administrators, making the procedure exorbitant and tedious. Christian et al [5] proposed a Malta software generated sketch database which contains the largest number of viewed software generated sketches. Wei Zhang et al [6] proposed automated face photo sketch recognition by developing advanced lassification algorithm to reduce the gap between the photos and sketches.…”

Section: Related Workmentioning

confidence: 99%

Face Photo Recognition using Sketch Image for Security System

2019

IJITEE

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Because of expanding requests in application regions, for example, validation of law requirement, video reconnaissance, banking and access to security frameworks, programmed face acknowledgment has pulled in extraordinary consideration as of late. The acknowledgment of the face has ended up being one of the present essential applications. It is utilized fundamentally for computerized amusement, security and authorization. Face acknowledgment can be for the entire or fractional face, the entire face for the entire face identification and explicit highlights for the incomplete face discovery. The examination in most criminal cases relies upon the portrayals attracted by the observers ' depiction. The programmed recovery of photographs from the police mug-shot database that coordinate this depiction can conceivably diminish the quantity of suspects. Fundamentally, this procedure relies upon the onlooker and precision of the sketch craftsman's memory of the face to catch those subtleties. It can help scientists viably find or diminish potential suspects. By and large, be that as it may, a speculate's photograph picture isn't accessible and the best substitute is frequently a sketch drawing dependent on an observer's memory. We present another photograph recuperation framework utilizing face draws by changing a photograph picture into a sketch, fundamentally lessening the distinction among photograph and sketch, enabling the two to coordinate successfully.

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“…Photos and sketches populate the gallery and probe sets, respectively, and the gallery is extended with the photos of 1521 subjects to simulate the extensive mug-shot galleries maintained by law-enforcement agencies, obtained from the MEDS-II, 4 FRGC v2.0, 5 Multi-PIE [44], and FEI 6 databases. 4 Available at: http://www.nist.gov/itl/iad/ig/sd32.cfm 5 Available at: http://www.nist.gov/itl/iad/ig/frgc.cfm 6 Available at: http://fei.edu.br/∼cet/facedatabase.html As shown in Table I, the FRSs are generally inferior to the intra-modality methods, which in turn typically lag behind the performance of the inter-modality approaches. The only exception is CBR, whose poor performance is likely a result of being designed to operate on software-generated sketches.…”

Section: System Fusionmentioning

confidence: 99%

“…Several state-of-the-art methods utilise hand-crafted features, such as the scale-invariant feature transform (SIFT) and multiscale local binary pattern (MLBP) [2]- [5]. However, these features are likely not optimal since they were not developed for intermodality face recognition [6], and it would therefore be desirable to employ descriptors that are better suited for the task of face photo-sketch recognition.…”

Section: Introductionmentioning

confidence: 99%

Forensic Face Photo-Sketch Recognition Using a Deep Learning-Based Architecture

Galea

Farrugia

2017

IEEE Signal Process. Lett.

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Numerous methods that automatically identify subjects depicted in sketches as described by eyewitnesses have been implemented, but their performance often degrades when using real-world forensic sketches and extended galleries that mimic law enforcement mug-shot galleries. Moreover, little work has been done to apply deep learning for face photo-sketch recognition despite its success in numerous application domains including traditional face recognition. This is primarily due to the limited number of sketch images available, which are insufficient to robustly train large networks. This letter aims to tackle these issues with the following contributions: 1) a state-of-the-art model pre-trained for face photo recognition is tuned for face photo-sketch recognition by applying transfer learning, 2) a three-dimensional morphable model is used to synthesise new images and artificially expand the training data, allowing the network to prevent over-fitting and learn better features, 3) multiple synthetic sketches are also used in the testing stage to improve performance, and 4) fusion of the proposed method with a state-of-the-art algorithm is shown to further boost performance. An extensive evaluation of several popular and state-of-the-art algorithms is also performed using publicly available datasets, thereby serving as a benchmark for future algorithms. Compared to a leading method, the proposed framework is shown to reduce the error rate by 80.7% for viewed sketches and lowers the mean retrieval rank by 32.5% for real-world forensic sketches.

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A Large-Scale Software-Generated Face Composite Sketch Database

Cited by 21 publications

References 19 publications

Learning from the Past: Meta-Continual Learning with Knowledge Embedding for Jointly Sketch, Cartoon, and Caricature Face Recognition

Learning from the Past: Meta-Continual Learning with Knowledge Embedding for Jointly Sketch, Cartoon, and Caricature Face Recognition

Face Photo Recognition using Sketch Image for Security System

Forensic Face Photo-Sketch Recognition Using a Deep Learning-Based Architecture

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