Forensic face photo-sketch recognition using a deep learning-based architecture

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.peer-reviewe

Matching software-generated sketches to face photographs with a very deep CNN, morphed faces, and transfer learning

Sketches obtained from eyewitness descriptions of criminals have proven to be useful in apprehending criminals, particularly when there is a lack of evidence. Automated methods to identify subjects depicted in sketches have been proposed in the literature, but their performance is still unsatisfactory when using software-generated sketches and when tested using extensive galleries with a large amount of subjects. Despite the success of deep learning in several applications including face recognition, little work has been done in applying it for face photograph-sketch recognition. This is mainly a consequence of the need to ensure robust training of deep networks by using a large number of images, yet limited quantities are publicly available. Moreover, most algorithms have not been designed to operate on software-generated face composite sketches which are used by numerous law enforcement agencies worldwide. This paper aims to tackle these issues with the following contributions: 1) a very deep convolutional neural network is utilised to determine the identity of a subject in a composite sketch by comparing it to face photographs and is trained by applying transfer learning to a state-of-the-art model pretrained for face photograph recognition; 2) a 3-D morphable model is used to synthesise both photographs and sketches to augment the available training data, an approach that is shown to significantly aid performance; and 3) the UoM-SGFS database is extended to contain twice the number of subjects, now having 1200 sketches of 600 subjects. An extensive evaluation of popular and stateof-the-art algorithms is also performed due to the lack of such information in the literature, where it is demonstrated that the proposed approach comprehensively outperforms state-of-the-art methods on all publicly available composite sketch datasets.peer-reviewe

Fusion of intra- and inter-modality algorithms for face-sketch recognition

Identifying and apprehending suspects by matching sketches created from eyewitness and victim descriptions to mugshot photos is a slow process since law enforcement agencies lack automated methods to perform this task. This paper attempts to tackle this problem by combining Eigentransformation, a global intra-modality approach, with the Eigenpatches local intra-modality technique. These algorithms are then fused with an inter-modality method called Histogram of Averaged Orientation Gradients (HAOG). Simulation results reveal that the intra- and inter- modality algorithms considered in this work provide complementary information since not only does fusion of the global and local intra-modality methods yield better performance than either of the algorithms individually, but fusion with the inter-modality approach yields further improvement to achieve retrieval rates of 94.05% at Rank-100 on 420 photo-sketch pairs. This performance is achieved at Rank-25 when filtering of the gallery using demographic information is carried out.peer-reviewe

A no-reference video quality metric using a natural video statistical model

The demand for high quality multimedia content is increasing rapidly, which has resulted in service providers employing Quality of Service (QoS) strategies to monitor the quality of delivered content. However, the QoS parameters commonly used do not correlate well with the actual quality perceived by the end-users. Numerous objective video quality assessment (VQA) metrics have been proposed to address this problem. However, most of these metrics rely on the availability of additional information from the original undistorted video to perform adequately, which will increase the bandwidth required. This paper presents a No-Reference (NR) VQA algorithm, which extracts a Natural Video Statistical Model using both spatial and temporal features to model the quality experienced by the end-users without needing additional information from the transmitter. These features are based on the observation that the statistics of natural scenes are regular on pristine content but are significantly altered in the presence of distortion. The proposed method achieves a Spearman Rank Order Correlation Coefficient (SROCC) of 0.8161 with subjective data, which is statistically identical and sometimes superior to existing state-of-the-art full and reduced reference VQA metrics.peer-reviewe

Complex factors in preconditioning a microarray gene

Preconditioning is complex, strong, evolutionary conserved cellular survival mechanism that is exhibited by different species as well as in different organs. A focused approach on microarray evaluation of preconditioning will be used to highlight the lack of clarity in investigating this complex phenomenon, exacerbated by the absence of a standardised terminology. This paper is an extensive review of the scientific literature on the investigation of preconditioning by means of a microarray approach. It dissects the design of the experiments used to investigate such phenomenon and classifies the complex factors in investigating preconditioning. It presents an attention to detail to the lexicon with a suggested classification and terminology that describes preconditioning that may help stratify and clarify research in this field.peer-reviewe

3D Scene Modeling from Dense Video Light Fields

International audienceLight field imaging offers unprecedented opportunities for advanced scene analysis and modelling, with potential applications in various domains such as augmented reality, 3D robotics, and microscopy. This paper illustrates the potential of dense video light fields for 3D scene modeling. We first recall the principles of plenoptic cameras and present a downloadable test dataset captured with a Raytrix 2.0 plenop-tic camera. Methods to estimate the scene depth and to construct a 3D point cloud representation of the scene from the captured light field are then described.

Cardiovascular magnetic resonance (CMR) and positron emission tomography (PET) imaging in the diagnosis and follow-up of patients with acute myocarditis and chronic inflammatory cardiomyopathy : A review paper with practical recommendations on behalf of the European Society of Cardiovascular Radiology (ESCR).

Advanced cardiac imaging techniques such as cardiovascular magnetic resonance (CMR) and positron emission tomography (PET) are widely used in clinical practice in patients with acute myocarditis and chronic inflammatory cardiomyopathies (I-CMP). We aimed to provide a review article with practical recommendations from the European Society of Cardiovascular Radiology (ESCR), in order to guide physicians in the use and interpretation of CMR and PET in clinical practice both for acute myocarditis and follow-up in chronic forms of I-CMP

A human phospholipid phosphatase activated by a transmembrane control module

In voltage-sensitive phosphatases (VSPs), a transmembrane voltage sensor domain (VSD) controls an intracellular phosphoinositide phosphatase domain, thereby enabling immediate initiation of intracellular signals by membrane depolarization. The existence of such a mechanism in mammals has remained elusive, despite the presence of VSP-homologous proteins in mammalian cells, in particular in sperm precursor cells. Here we demonstrate activation of a human VSP (hVSP1/TPIP) by an intramolecular switch. By engineering a chimeric hVSP1 with enhanced plasma membrane targeting containing the VSD of a prototypic invertebrate VSP, we show that hVSP1 is a phosphoinositide-5-phosphatase whose predominant substrate is PI(4,5)P(2). In the chimera, enzymatic activity is controlled by membrane potential via hVSP1\u27s endogenous phosphoinositide binding motif. These findings suggest that the endogenous VSD of hVSP1 is a control module that initiates signaling through the phosphatase domain and indicate a role for VSP-mediated phosphoinositide signaling in mammals

COVID-19 and Substance Use Disorders:Recommendations to a Comprehensive Healthcare Response. An International Society of Addiction Medicine (ISAM) Practice and Policy Interest Group Position Paper

Coronavirus disease 2019 (COVID-19) is escalating all over the world and has higher morbidities and mortalities in certain vulnerable populations. People Who Use Drugs (PWUD) are a marginalized and stigmatized group with weaker immunity responses, vulnerability to stress, poor health conditions, high-risk behaviors, and lower access to health care services. These conditions put them at a higher risk of COVID-19 infection and its complications. In this paper, an international group of experts on addiction medicine, infectious diseases, and disaster psychiatry explore the possible raised concerns in this issue and provide recommendations to manage the comorbidity of COVID-19 and Substance Use Disorder (SUD).Publisher PDFPeer reviewe

Reaching out for help : calls to a mental health helpline prior to and during the COVID-19 Pandemic

The COVID-19 pandemic is a major health crisis associated with adverse mental health consequences. This study examined 2908 calls made to a national mental health helpline over a 10 month period, 2 months prior to (Pre-COVID) and 8 months during the pandemic phase, that incorporated the imposition of a partial lockdown, followed by the removal and reintroduction of restrictive measures locally. Data collected included reason/s for call assistance, gender, age and number of daily diagnosed cases and deaths due to COVID-19. In the Pre-COVID phase, calls for assistance were related to information needs and depression. With the imposition of a partial lockdown, coupled with the first local deaths and spikes in number of diagnosed cases, a significant increase in number of calls targeting mental health, medication management and physical and financial issues were identified. Following the removal of local restrictions, the number of calls decreased significantly; however, with the subsequent reintroduction of restrictions, coupled with the rise in cases and deaths, assistance requested significantly targeted informational needs. Hence, whilst calls in the initial phase of the pandemic mainly targeted mental health issues, over time this shifted towards information seeking requests, even within a context where the number of deaths and cases had significantly risen