In hand-based biometrics, fingerprint, finger vein, finger knuckle print, palm print, palm vein, dorsal hand vein, and hand shape are the traits that are getting much attention. However, finger shape (FS), a forgettable trait, has not been studied specifically for identification purposes. In this work, we explore this content as a complement to the hand-based biometrics. Firstly, we annotate the FS on a publicly available finger vein dataset as the ground truth for finger semantic segmentation. Then we explore the finger semantic segmentation task on the annotated data and propose a lightweight network, namely FinSeg-Net (finger segmentation network). Finally, we conduct the FS authentication experiment based on four matching methods; experimental results show that the FS traits can achieve identity authentication. This work is the first study for FS biometrics specifically, and built the first FS dataset, which will be accessed via: https://github.com/SCUT-BIP-Lab/FinSeg.

Empathic medical conversation is central to patient-centered care within Narrative Medicine. However, difficulties, such as physicians’ limited empathic capabilities and lack of time, impede the practice. Research on real-time, on-site empathic medical exchanges has been limited in exploring technology to assist and enhance physicians’ capabilities. This paper proposed the Empathic Opportunity Perception and Distinction (EOPD) framework for building physician-AI collaboration based on Intelligence Augmentation (IA) for empathic conversations. The EOPD integrates two multi-modal machine learning (ML) models based on facial and verbal cues, presenting a physician-AI interaction framework and three distinctive visualization components: emotional reference, opportunity reminding and keyword collection, and situation understanding. To assess EOPD's effectiveness and gauge physicians’ and patients’ receptiveness, a prototype system named EMVIS ( EM otional VIS ualization ) was designed and developed. Results from the study demonstrated improvements in physicians’ empathy efforts and perceived empathy performance when using EMVIS, particularly for junior physicians. Physicians and patients held positive attitudes towards EMVIS, with patients expressing a high expectation that EMVIS would improve the physician-patient relationship. The research showed the efficacy of the multi-modal ML models in supporting complex affective empathy and EMVIS in facilitating and complementing empathy concerns. It highlighted the tailored support to junior and senior physicians and emphasized physician-AI collaboration to maintain user autonomy and mitigate potential biases. Future research should explore extensive system applications, tailor visual and interactive support for physicians, and implement adaptive and reflective ML models to improve the effectiveness and efficiency of empathy communications.

More palm features, such as veins and shapes obtained from an enlarged contactless palm vein region of interest (ROI), have been shown to improve recognition performance. However, a few efforts have been made to adequately utilize these features for mining identity information. To address this issue, we propose a Region-Specific Network (RSNet) for contactless palm vein authentication. Our RSNet is a dual-branch structure for global and local feature extraction. Firstly, a Region-based Local feature Enhancement Block (RLEB) is proposed at the local branch to extract region-specific features. In the RLEB, the intermediate feature maps are divided into three asymmetrical patches based on the physiological characteristics of palm vein and palm shape for extracting diversified features, enhancing the local feature representation. Then, a Multi-scale Aggregation Block (MAB) is proposed that efficiently aggregates multi-scale features at a more granular level. Furthermore, to guide the global and local branches in learning complementary feature aspects, a difference loss is introduced to apply a soft subspace orthogonality constraint between the global and local vectors during training. The global branch is designed to assist the learning process of local features, without being adopted for inference. Extensive experiments have demonstrated the effectiveness and superiority of our method, and the RSNet achieves new State-Of-The-Art (SOTA) authentication performance on seven public contactless palm vein databases in the open-set scenario.

Finger vein biometrics is becoming an important source of human authentication due to its advantages in terms of liveness detection, high security, and user convenience. Although there exist a lot of deep learning-based methods for finger vein authentication, they only extract features from finger vein images in the spatial domain and may lose some important information that is present in other domains, such as the frequency domain. Motivated by this conjecture and the remarkable performance of image feature extraction in the frequency domain, this work explores a method capable of extracting finger vein features in both the spatial and frequency domains. Therefore, the features extracted from different domains can complement each other. In addition, we propose a novel frequency-spatial coupling network (FVFSNet) for finger vein authentication. FVFSNet is mainly composed of three parts: (1) the frequency domain processing module (FDPM), (2) the spatial domain processing module (SDPM), and (3) the frequency-spatial coupling module (FSCM). The FDPM is used to extract the finger vein features present in the frequency domain, which is mainly composed of the frequency-spatial domain transformation and the frequency domain convolution layer. The SDPM is used to extract the finger vein features present in the spatial domain, which is mainly composed of convolution layers with an efficient design. The FSCM is used to couple the features extracted from the FDPM and SDPM, which is mainly composed of the channel and spatial attention mechanisms. To validate our conjecture and the performances of FVFSNet, extensive experiments are conducted on nine commonly used publicly available finger vein datasets. Experimental results show that the frequency domain constitutional neural network has a surprising effect on finger vein authentication, and the proposed FVFSNet achieves the state-of-the-art performance with the advantages of lightweight and low computational cost.

Finger contains several discriminative biometric traits, including fingerprint, finger vein, finger knuckle, and finger shape, which are complementary in identity information. However, in most current researches and practical applications, only a single or several traits are utilized, which are prone to unsatisfactory recognition performance and easy forgery. Our work is the first attempt to collect and study all biometric traits on the finger. Firstly, a novel multi-view, multi-spectral 3D finger imaging system is designed. To the best of our knowledge, it is the first biometric imaging system that can capture almost all finger-based traits. With this 3D finger imaging system, we scanned numerous fingers, acquiring their external skin images and internal vein images from 6 different views. Then 3D finger models with skin and vein textures are reconstructed by space carving, mesh regularization, and texture mapping algorithms. Secondly, we establish a benchmark dataset, namely the Large- scale Finger Multi-Biometric database and benchmark for 3D Finger Biometrics (LFMB-3DFB). LFMB-3DFB contains 695 fingers, and each finger is captured 10 times. Then, 6 finger skin images and 6 finger vein images are obtained for each acquisition, and final 83,400 images and 6,950 3D finger models are obtained. Besides, we designed a more rigorous and comprehensive evaluation protocol for both identification and verification tasks. Finally, we designed corresponding baselines for 2D finger traits recognition, multi-view finger traits recognition, 3D finger traits recognition, and score-level fusion. Rigorous experiments have been conducted to verify the significance and usefulness of the proposed LFMB-3DFB.

In the United States, about 600,000 people die of heart disease every year. The annual cost of care services, medications, and lost productivity reportedly exceeds 108.9 billion dollars. Effective disease risk assessment is critical to prevention, care, and treatment planning. Recent advancements in text analytics have opened up new possibilities of using the rich information in electronic medical records (EMRs) to identify relevant risk factors. The 2014 i2b2/UTHealth Challenge brought together researchers and practitioners of clinical natural language processing (NLP) to tackle the identification of heart disease risk factors reported in EMRs. We participated in this track and developed an NLP system by leveraging existing tools and resources, both public and proprietary. Our system was a hybrid of several machine-learning and rule-based components. The system achieved an overall F1 score of 0.9185, with a recall of 0.9409 and a precision of 0.8972.

There is a growing need for managing importance of entities in knowledge system in order to realize the full potential of knowledge. How to calculate the importance of entities automatically is the primary issue. We argue that importance of entities in knowledge is dynamic, the importance is changed along with the using of ontology; and different groups of user have different criteria of importance. In this paper, a novel weight assignment method which takes usage and structure properties of ontology into account is proposed. When considering the usage information of ontology, we analyze paths that are used to respond to queries; and use the frequency of entities included in the paths to produce the optimal weight assignment for the assumption of high importance of entities which included in paths that respond to queries. After get the initial weight of entities, a pervasion algorithm which considers the structure of ontology is used to compute the final weight of entities. Weight of a node is high if the node has many incoming links and the incoming links and nodes which these links are from have high scores. Experiment show effectiveness of this weight assignment method.

The problems of adaptive quantization and prediction in a DPCM system are analyzed. It is shown that while quantizing speech signal with an adaptive algorithm, the probability distribution of Piassociated with the quantization intervals is not uniquely determined by the PDF of speech, but can be controlled by the adaptive parameters (Gi) and algorithm of adaptation. Thus, speech signal can be quantized by an optimum quantization charateristic (OGC) designed to match a specific power-limited PDF in order to meet certain SNR and entropy requirements. This paper derives a PDF with the maximum SNR which is greater than that of Gaussian PDF in 0.5dB. Moreover, the calculation of SNR in an adaptive quantizer and the method for searching adaptive parameters (Gi) are discussed. Finally, a constant-increment sequential adaptive prediction algorithm is developed. It removes multiplier with a prediction gain loss less than 0.5dB.