Eyewitness Face Sketch Recognition Based on Two-Step Bias Modeling

Nejati, Hossein; Zhang, Li; Sim, Terence

doi:10.1007/978-3-642-40246-3_4

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…In real world, many different artists can draw sketches of a same person and their sketches are very different from each other both in texture and shape. This is mainly caused by biases added by artists with different perceptual and drawing styles [2], [3]. Because of these biases caused by artists, sketches drawn by an artist have unique characteristics.…”

Section: Introductionmentioning

confidence: 99%

Efficient Generation of Multiple Sketch Styles Using a Single Network

et al. 2019

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In the real world, different artists draw sketches of the same person with different artistic styles both in texture and shape. Our goal is to synthesize realistic face sketches of different styles while retaining the input face identity, only using a single network. To achieve this, we employ a modified conditional GAN with a target style label as input. Our method is capable of synthesizing multiple sketch styles even though it is based on a single network. Sketches created by our method show sketch quality comparable to the state-of-the-art sketch synthesis methods that use multiple networks.

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Section: Introductionmentioning

confidence: 99%

Efficient Generation of Multiple Sketch Styles Using a Single Network

et al. 2019

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“…Some enhance the current manual or semi-automatic recognition approaches used by law enforcement agencies and some tackle new demands from sexual offenses, riots, digital crimes, and terrorism. They include, but are not limited to, latent print enhancement [23][24], tattoo retrieval [25][26][27], face sketch recognition [28][29][30][31], shoe print recognition [32], dental identification [33][34], authorship verification [35], body skin mark identification and related studies [36][37][38], androgenic hair patterns matching [4], soft biometrics and witness descriptions [5][6][7], and vein patterns matching [8].…”

Section: Some Computational Methodsmentioning

confidence: 99%

“…Nejati et al [31] introduced a recognition method to estimate and reduce distortions due to personal sketching bias before attempting to recognize the sketch.…”

Section: Some Computational Methodsmentioning

confidence: 99%

Criminal and victim identification based on soft biometrics

Chan¹

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Criminal and victim identification is always important in forensic investigation. However, it can be a very challenging problem for identifying criminals and victims in digital media when only their non-facial body sites are available in evidence images. These criminals and victims can be masked gunmen, paedophiles, and victims in child pornographic and voyeur images.To solve the above problem, several novel alignment and identification approaches are proposed in this thesis. Firstly, lower leg geometry is proposed as a soft biometric trait for criminal and victim identification. This study provides a foundation for further research based on body geometry. Secondly, leg geometry and hair follicles are proposed to align the androgenic hair patterns in consideration of viewpoint and pose variations, which were ignored by a recent paper suggesting androgenic hair patterns for identification.Experiments on 1,138 high and low resolution images from 283 different legs show that the proposed alignment algorithms provide improvements of 5%-10% on different experimental settings.Thirdly, a new approach is developed to improve the identification of androgenic hair patterns significantly. In the past, it was believed that androgenic hair patterns in low resolution images are not a distinctive biometric trait because of the previous result. A new algorithm, which makes use of leg geometry to align lower leg images, large feature sets (about 60,000 features) extracted through multi-directional gridding systems to increase discriminative power and robustness, the partial least squares (PLS) method to handle imbalanced training data and to perform the multi-grid feature fusion, and scheme I thank my fellow labmates in Biometrics and

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