• Novel QCERN framework clusters and ranks image quality effectively. • Incorporates Order, Metric, and Center Loss for precise alignment. • Demonstrates superior generalization across diverse datasets. • Offers applications in photography, medical imaging, and surveillance. • Utilizes dynamic score anchors for improved accuracy and adaptability. This paper presents a new technology that focuses on blind image quality assessment (BIQA) through a framework known as Quality-Centric Embedding and Ranking Network (QCERN). The framework ensures maximum efficiency when processing images under various possible scenarios. QCERN is entirely different from contemporary BIQA techniques, which focus solely on regressing quality scores without structured embeddings. In contrast, the proposed model features a well-defined embedding space as its principal focus, in which picture quality is both clustered and ordered. This dynamic quality of images enables QCERN to utilize several adaptive ranking transformers along a geometric space populated by dynamic score anchors representing images of equivalent quality QCERN features a distinct advantage since unlabeled images of interest can be placed by evaluation of their distance to these specified score anchors inductively in the embedding space, improving accuracy as well as generalization across disparate datasets. Multiple loss functions are utilized in this instance, including order and metric loss, to ensure that images are positioned correctly according to their quality while maintaining distinct divisions of quality. With the application of QCERN, numerous experiments have demonstrated its ability to outperform existing models by consistently delivering high-quality predictions across various datasets, making it a competitive option. This quality-centric embedding and ranking methodology is excellent for reliable quality assessment applications, such as in photography, medical imaging, and surveillance.

In the era of remote sensing (RS), the demand for accurate land cover classification (LCC) has intensified due to various environmental challenges such as deforestation and urbanization. Conventional approaches often rely on shallow features for classification, limiting their effectiveness in capturing spatial patterns and diverse land cover types. In response, this study introduces a novel LCC approach utilizing a convolutional neural network (CNN) equipped with a dual land cover attention segment. The proposed module integrates channel attention (CA) and spatial attention mechanisms (SA) to enhance the discriminative capabilities of deep models. Leveraging inter-channel and inter-spatial relationships, the dual attention module enables the identification of various land cover types, spatial patterns, and color variations. Through thorough experimentation, the InceptionV3 feature extractor was identified as the optimal backbone for the proposed network architecture. Furthermore, to address the challenge of diverse land cover types, highly curated datasets are utilized. Additionally, to optimize model efficiency and reduce size, an improved model compression approach is employed. The effectiveness of the proposed Dual Land Cover Attention Network (DLAN) was evaluated through extensive experimentation, demonstrating superior performance compared to conventional methods. The results indicate the potential of DLAN in advancing LCC tasks, facilitating detailed agricultural zoning, environmental monitoring, and urban planning at a regional scale.

The deep integration of the Internet of Things (IoT) and the medical industry has given birth to the Internet of Medical Things (IoMT). In IoMT, physicians treat a patient's disease by analyzing patient data collected through mobile devices with the assistance of an artificial intelligence (AI)-empowered systems. However, the traditional AI technologies may lead to the leakage of patient privacy data due to its own design flaws. As a privacy-preserving federated learning (FL) can generate a global disease diagnosis model through multiparty collaboration. However, FL is still unable to resist inference attacks. In this article, to address such problems, we propose a privacy-enhanced disease diagnosis mechanism using FL for IoMT. Specifically, we first reconstruct medical data through a variational autoencoder and add differential privacy noise to it to resist inference attacks. These data are then used to train local disease diagnosis models, thereby preserving patients' privacy. Furthermore, to encourage participation in FL, we propose an incentive mechanism to provide corresponding rewards to participants. Experiments are conducted on the arrhythmia database Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH). The experimental results show that the proposed mechanism reduces the probability of reconstructing patient medical data while ensuring high-precision heart disease diagnosis.

We propose an authentication methodology that combines multimodal biometrics and cryptographic mechanisms for border control applications. We accommodate faces and fingerprints without a mandatory requirement of (tamper-resistant) smart-card-level devices on e-passports for easier deployment. It is even allowable to imprint (publicly readable) bar codes on the passports. Additionally, we present a solution based on the certification and key management method to control the validity of passports within the current Public-Key Infrastructure (PKI) technology paradigm.

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.

Two key performance characterization of biometric algorithms (face recognition in particular) are (1) verification performance and (2) and performance as a function of database size and composition. This characterization is required for developing robust face recognition algorithms and for successfully transitioning algorithms from the laboratory to real world. In this paper we (1) present a general verification protocol and apply it to the results from the Sep96 FERET test, and (2) discuss and present results on the effects of database size and variability on identification and verification performance.

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1199 individuals are included in the FERET database, which is divided into development and sequestered portions. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to (1) assess the state of the art, (2) identify future areas of research, and (3) measure algorithm performance on large databases.