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Feature extractors significantly impact the performance of biometric systems. In the field of hand gesture authentication, existing studies focus on improving the model architectures and behavioral characteristic representation methods to enhance their feature extractors. However, loss functions, which can guide extractors to produce more discriminative identity features, are neglected. In this paper, we improve the margin-based Softmax loss functions, which are mainly designed for face authentication, in two aspects to form a new loss function for hand gesture authentication. First, we propose to replace the commonly used cosine function in the margin-based Softmax losses with a linear function to measure the similarity between identity features and proxies (the weight matrix of Softmax, which can be viewed as class centers). With the linear function, the main gradient magnitude decreases monotonically as the quality of the model improves during training, thus allowing the model to be quickly optimized in the early stage and precisely fine-tuned in the late stage. Second, we design an adaptive margin scheme to assign margin penalties to different samples according to their separability and the model quality in each iteration. Our adaptive margin scheme constrains the gradient magnitude. It can reduce radical (excessively large) gradient magnitudes and provide moderate (not too small) gradient magnitudes for model optimization, contributing to more stable training. The linear function and the adaptive margin scheme are complementary. Combining them, we obtain the proposed linear adaptive additive angular margin (L3AM) loss. To demonstrate the effectiveness of L3AM loss, we conduct extensive experiments on seven hand-related authentication datasets, compare it with 25 state-of-the-art (SOTA) loss functions, and apply it to eight SOTA hand gesture authentication models. The experimental results show that L3AM loss further improves the performance of the eight authentication models and outperforms the 25 losses. The code is available at https://github.com/SCUT-BIP-Lab/L3AM. [ABSTRACT FROM AUTHOR]

Motivation Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. Results We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r 2) on 27/33 datasets, with an average improvement of 7.2%–22.9% against the state-of-the-art bioactivity prediction methods. Availability and implementation The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC. [ABSTRACT FROM AUTHOR]

The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein–ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein–ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization. [ABSTRACT FROM AUTHOR]

Ferry contributing a significant amount of greenhouse gas is one of the critical vessels to be electrified. Designing a power system for a ferry with hybrid-shaft generators is different from designing a power system for other vessels because of its fixed route. More clearly, ferries repeatedly travel between their port of origin and port of destination, and before the next voyage, the battery must be recharged to the initial state so that the optimal energy management scheme can be repeatedly applied. Furthermore, the flexibility of hybrid-shaft generators, which allow more fuel saving, increases design complexity. In this paper, a mixed-integer non-linear programming problem is first formulated, and a power management algorithm with an initialization step for fulfilling the battery recharging requirement and a refinement step for minimizing the fuel consumption is proposed. The simulation results obtained from data of an actual ferry show that the proposed power management algorithm can fully recharge the battery and consumes less fuel than a rule-based power management scheme. Simulations also reveal that fuel consumption depends on available shore power, highlighting the necessity to develop charging infrastructure for practical electrification. Because of its speed, the algorithm can support hardware sizing, e.g., battery sizing. [ABSTRACT FROM AUTHOR]

Accurately diagnosing and describing the severity of vitiligo is crucial for prognostication, treatment selection and comparison. Currently, disease severity scores require dermatologists to estimate percentage area of involvement, which is subjected to inter and intra-assessor variability. Previous studies focus on pure skin but vitiligo on the face, which has a more serious impact on patients’ quality of life, was completely neglected. Convolutional neural networks (CNNs) have good performance on many segmentation tasks. However, due to data privacy, it is hard to have a large clinical vitiligo face image dataset to train a CNN. To address this challenge, images from two different sources, the Internet and the proposed vitiligo face synthesis algorithm, are employed in training. 843 vitiligo images taken from different viewpoints were collected from the Internet. These images are hugely different from the target clinical images collected according to a newly established international standard. To have more vitiligo face images similar to the target clinical images to enhance segmentation performance, an image synthesis algorithm is proposed. Both synthetic and Internet images are used to train a CNN which is modified from the fully convolutional network (FCN) to segment face vitiligo lesions. The results show that 1) the synthetic images effectively improve segmentation performance; 2) the proposed algorithm achieves 1.06 % error for the face vitiligo area estimation and 3) it is more accurate than two dermatologists and all the previous automated vitiligo segmentation methods, which were designed for segmentation vitiligo on pure skin. [ABSTRACT FROM AUTHOR]

Tattooing portraits of loved ones is a popular form of love expression and tribute. Tattooing portraits is complicated and challenging because of detailed facial expressions and unique characters of each person. Currently, it is hard for clients to give clear instructions on tattoo designs to tattooists, because there is no effective way to see a portrait tattoo before putting it on the body. In this paper, an algorithm which transforms a given portrait photo to a portrait tattoo is proposed. It takes a portrait photo, a reference portrait tattoo image, a skin image and a set of parameters as inputs. The portrait photo is the person's face whom the client wants to put on his/her skin. The reference portrait tattoo image is used to control the color and style of the synthetic portrait tattoo. The skin image is taken from the skin region where the client wants to tattoo. By adjusting the parameters, portrait tattoos with different characteristics can be generated. The proposed algorithm uses a series of tailor-made image processing methods and a digital tattoo needle model to perform digital tattooing on the skin image. Comparing with the state-of-the-art style transfer methods, the proposed algorithm produces more realistic portrait tattoos. [ABSTRACT FROM AUTHOR]

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods. [ABSTRACT FROM AUTHOR]

As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys. [ABSTRACT FROM AUTHOR]

Background: Hyperpigmentation has varied aetio‐pathologies. Hence, accurate and reproducible diagnosis of the type of hyperpigmentation is essential for effective management. It is typically made clinically by dermatologists but the rate of inter‐ and intra‐observer agreement/variability is unknown. Hyperpigmented facial lesions are extremely common but access to dermatological services is difficult or costly in most countries. Thus, it is desired to evaluate dermatologists' inter‐ and intra‐observer agreement in the diagnosis and to develop an algorithm for automated diagnosis. Materials and Methods: Hyperpigmented lesions on 392 facial images were diagnosed by three experienced dermatologists either jointly or independently, and this process was subsequently repeated for 52 randomly selected images. When there was non‐concordance amongst the dermatologists for the diagnosis, a majority decision was taken as correct diagnosis. Inter‐observer and intra‐observer agreement were analysed for the diagnosis of the hyperpigmented lesions. Thereafter, a multiclass classification method was developed to perform the task in an automatic manner. The developed algorithm was compared and validated against the ground truth derived from the dermatologists. Results: Both inter‐ and intra‐observer agreements are in the moderate range. The algorithm agreed well with the derived ground truth, with a Kappa value of 0.492, which is similar to the Kappa values of inter‐ and intra‐ observer agreements. Conclusion: The rates of inter‐ and intra‐observer agreement in the diagnosis of hyperpigmented facial lesions amongst dermatologists were moderate in this study. Compared to visual assessment from the dermatologists, automated diagnosis using the developed algorithm achieved a high rate of concordance. [ABSTRACT FROM AUTHOR]

In child sexual exploitation offenses, the collected evidence images often show the skin of nonfacial body parts of the criminals and victims. For identification in this scenario, 'relatively permanent pigmented or vascular skin marks,' abbreviated as RPPVSM, were recently introduced as the basis for a novel biometric trait. This pilot study evaluated the interexaminer variability of RPPVSM identification. Four dermatology physicians were recruited to examine RPPVSM from 75 skin images collected from a total of 51 Caucasian and Asian subjects. The images were separated into 50 reference ('suspect') images and 25 evaluation ('evidence') images. The examiners were asked to perform identification by annotating RPPVSM in each of the 25 evaluation images and matching them with the reference images. The rate of misidentification was 0% while the mean rate at which examiners failed to find a match was 6%, indicating the potential of dermatology physicians performing the role of RPPVSM examiners. [ABSTRACT FROM AUTHOR]

Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM) were recently introduced as a biometric trait for identification in the cases in which the evidence images show only the nonfacial body parts of the criminals or victims, such as in child sexual abuse and riots. As manual RPPVSM identification is tiring and time-consuming, an automated RPPVSM identification system is proposed in this paper. The system comprises skin segmentation, RPPVSM detection, and RPPVSM matching algorithms. The system was evaluated on 1,200 back images collected from 283 Asian and Caucasian subjects in varying pose and viewpoint conditions. The system achieved rank-1 and rank-10 identification accuracies of 76.79% and 88.97%, respectively, higher than the identification accuracies given by existing skin mark detection methods previously proposed for face recognition systems. To handle identification with limited numbers of RPPVSM, a fusion scheme with inferred vein patterns is also proposed. The fusion was evaluated on 2,360 images of chests, forearms, and thighs collected mostly from Asian subjects, who tend to have fewer RPPVSM than Caucasian subjects. The results show that the fusion improves vein identification in all body parts with improvement rates varying between 2% and 5% depending on the number of RPPVSM detected. To the best of our knowledge, this is the first work on automated identification in color skin images based on nonfacial skin marks and fusion with inferred vein patterns in forensic settings. [ABSTRACT FROM PUBLISHER]

IrisCode has been used to gather iris data for 430 million people. Because of the huge impact of IrisCode, it is vital that it is completely understood. This paper first studies the relationship between bit probabilities and a mean of iris images (The mean of iris images is defined as the average of independent iris images.) and then uses the Chi-square statistic, the correlation coefficient and a resampling algorithm to detect statistical dependence between bits. The results show that the statistical dependence forms a graph with a sparse and structural adjacency matrix. A comparison of this graph with a graph whose edges are defined by the inner product of the Gabor filters that produce IrisCodes shows that partial statistical dependence is induced by the filters and propagates through the graph. Using this statistical information, the security risk associated with two patented template protection schemes that have been deployed in commercial systems for producing application-specific IrisCodes is analyzed. To retain high identification speed, they use the same key to lock all IrisCodes in a database. The belief has been that if the key is not compromised, the IrisCodes are secure. This study shows that even without the key, application-specific IrisCodes can be unlocked and that the key can be obtained through the statistical dependence detected. [ABSTRACT FROM PUBLISHER]

Identifying individuals in evidence images, where their faces are covered or obstructed, is a challenging task. In the legal case, United States v. Michael Joseph Pepe (2008), Craft and Kong, who served as expert witnesses, used pigmented skin marks to identify a suspect in evidence images. Their expert opinions were challenged, partially because the blocking artifacts generated by the standard JPEG algorithm adversely affect the visibility of the small skin marks. In addition to this case, a huge amount of JPEG-compressed child pornography is posted online every day. Although many methods have been developed to remove blocking artifacts, they are ineffective for our target application. In this paper, a knowledge-based (KB) approach, which simultaneously removes JPEG blocking artifacts, and recovers skin features, is proposed. Given a training set containing both original and compressed skin images, the relationship between original blocks and compressed blocks can be established. This prior information is used to infer the original blocks of compressed evidence images. A Markov-model-based algorithm and a faster one-pass algorithm were developed to make inference, and a block synthesis algorithm was developed to handle the cases where compressed blocks are not contained in the training set. An indexing mechanism was also proposed to deal with large datasets efficiently. Extensive experiments were conducted on images with different characteristics and compression ratios. Both subjective and objective evaluations demonstrated that the KB approach is more effective than other methods. In summary, the KB approach is capable of removing blocking artifacts to recover useful skin features. [ABSTRACT FROM PUBLISHER]

IrisCode is an iris recognition algorithm developed in 1993 and continuously improved by Daugman. Understanding IrisCode's properties is extremely important because over 60 million people have been mathematically enrolled by the algorithm. In this paper, IrisCode is proved to be a compression algorithm, which is to say its templates are compressed iris images. In our experiments, the compression ratio of these images is 1:655. An algorithm is designed to perform this decompression by exploiting a graph composed of the bit pairs in IrisCode, prior knowledge from iris image databases, and the theoretical results. To remove artifacts, two postprocessing techniques that carry out optimization in the Fourier domain are developed. Decompressed iris images obtained from two public iris image databases are evaluated by visual comparison, two objective image quality assessment metrics, and eight iris recognition methods. The experimental results show that the decompressed iris images retain iris texture that their quality is roughly equivalent to a JPEG quality factor of 10 and that the iris recognition methods can match the original images with the decompressed images. This paper also discusses the impacts of these theoretical and experimental findings on privacy and security. [ABSTRACT FROM AUTHOR]