Recognition Scenarios Research Articles

Ship Infrared Automatic Target Recognition Based on Bipartite Graph Recommendation: A Model-Matching Method

Deep learning technology has greatly propelled the development of intelligent and information-driven research on ship infrared automatic target recognition (SIATR). In future scenarios, there will be various recognition models with different mechanisms to choose from. However, in complex and dynamic environments, ship infrared (IR) data exhibit rich feature space distribution, resulting in performance variations among SIATR models, thus preventing the existence of a universally superior model for all recognition scenarios. In light of this, this study proposes a model-matching method for SIATR tasks based on bipartite graph theory. This method establishes evaluation criteria based on recognition accuracy and feature learning credibility, uncovering the underlying connections between IR attributes of ships and candidate models. The objective is to selectively recommend the optimal candidate model for a given sample, enhancing the overall recognition performance and applicability of the model. We separately conducted tests for the optimization of accuracy and credibility on high-fidelity simulation data, achieving Accuracy and EDMS (our credibility metric) of 95.86% and 0.7781. Our method improves by 1.06% and 0.0274 for each metric compared to the best candidate models (six in total). Subsequently, we created a recommendation system that balances two tasks, resulting in improvements of 0.43% (accuracy) and 0.0071 (EDMS). Additionally, considering the relationship between model resources and performance, we achieved a 28.35% reduction in memory usage while realizing enhancements of 0.33% (accuracy) and 0.0045 (EDMS).

Indoor Synthetic Data Generation: A Systematic Review

Objective:Deep learning-based object recognition, 6D pose estimation, and semantic scene understanding require a large amount of training data to achieve generalization. Time-consuming annotation processes, privacy, and security aspects lead to a scarcity of real-world datasets. To overcome this lack of data, synthetic data generation has been proposed, including multiple facets in the area of domain randomization to extend the data distribution. The objective of this review is to identify methods applied for synthetic data generation aiming to improve 6D pose estimation, object recognition, and semantic scene understanding in indoor scenarios. We further review methods used to extend the data distribution and discuss best practices to bridge the gap between synthetic and real-world data. Methods:We adhered to the guidelines of the systematic PRISMA technique. Three databases, IEEE Xplore, Springer Link, and ACM, and an additional manual search were conducted. In total, we identified 241 studies and included 34 in our systematic review. Conclusion:In summary, synthetic data generation has been performed using crop-out methods, graphic APIs, 3D modeling or authoring tools, or game engine-based methods. To extend the data distribution, varying scene parameters, i.e., lighting conditions or textures and the use of distracting objects in the scene are promising.

Fusion recognition of palmprint and palm vein based on modal correlation.

Biometric authentication prevents losses from identity misuse in the artificial intelligence (AI) era. The fusion method integrates palmprint and palm vein features, leveraging their stability and security and enhances counterfeiting prevention and overall system efficiency through multimodal correlations. However, most of the existing multi-modal palmprint and palm vein feature extraction methods extract only feature information independently from different modalities, ignoring the importance of the correlation between different modal samples in the class to the improvement of recognition performance. In this study, we addressed the aforementioned issues by proposing a feature-level joint learning fusion approach for palmprint and palm vein recognition based on modal correlations. The method employs a sparse unsupervised projection algorithm with a "purification matrix" constraint to enhance consistency in intra-modal features. This minimizes data reconstruction errors, eliminating noise and extracting compact, and discriminative representations. Subsequently, the partial least squares algorithm extracts high grayscale variance and category correlation subspaces from each modality. A weighted sum is then utilized to dynamically optimize the contribution of each modality for effective classification recognition. Experimental evaluations conducted for five multimodal databases, composed of six unimodal databases including the Chinese Academy of Sciences multispectral palmprint and palm vein databases, yielded equal error rates (EER) of 0.0173%, 0.0192%, 0.0059%, 0.0010%, and 0.0008%. Compared to some classical methods for palmprint and palm vein fusion recognition, the algorithm significantly improves recognition performance. The algorithm is suitable for identity recognition in scenarios with high security requirements and holds practical value.

Subnetwork-Lossless Robust Watermarking for Hostile Theft Attacks in Deep Transfer Learning Models

Recently, considerable progress has been made in providing solutions to prevent intellectual property (IP) theft for deep neural networks (DNNs) in ideal classification or recognition scenarios. However, little work has been dedicated to protecting the IP of DNN models in the context of transfer learning. Moreover, knowledge transfer is usually achieved through knowledge distillation or cross-domain distribution adaptation techniques, which will easily lead to the failure of the IP protection due to the high risk of the underlying DNN watermark being corrupted. To address this issue, we propose a subnetwork-lossless robust DNN watermarking (SRDW) framework, which can exploit out-of-distribution (OOD) guidance data augmentation to boost the robustness of watermarking. Specifically, we accurately seek the most rational modification structure (i.e., core subnetwork) using the module risk minimization, and then calculate the contrastive alignment error and the corresponding hash value as the reversible compensation information for the restoration of carrier network. Experimental results show that our scheme has superior robustness against various hostile attacks, such as fine-tuning, pruning, cross-domain matching, and overwriting. In the absence of malicious jamming attacks, the core subnetwork can be recovered without any loss. Besides that, we investigate how embedding watermarks in batch normalization (BN) layers affect the generalization performance of the deep transfer learning models, which reveals that reducing the embedding modifications in BN layers can further promote the robustness to resist hostile attacks.

A Dual-Adversarial Model for Cross-time and Cross-subject Cognitive Workload Decoding.

Electroencephalogram (EEG) signals are widely utilized in the field of cognitive workload decoding (CWD). However, when the recognition scenario is shifted from subject-dependent to subject-independent or spans a long period, the accuracy of CWD deteriorates significantly. Current solutions are either dependent on extensive training datasets or fail to maintain clear distinctions between categories, additionally lacking a robust feature extraction mechanism. In this paper, we tackle these issues by proposing a Bi-Classifier Joint Domain Adaptation (BCJDA) model for EEG-based cross-time and cross-subject CWD. Specifically, the model consists of a feature extractor, a domain discriminator, and a Bi-Classifier, containing two sets of adversarial processes for domain-wise alignment and class-wise alignment. In the adversarial domain adaptation, the feature extractor is forced to learn the common domain features deliberately. The Bi-Classifier also fosters the feature extractor to retain the category discrepancies of the unlabeled domain, so that its classification boundary is consistent with the labeled domain. Furthermore, different adversarial distance functions of the Bi-Classifier are adopted and evaluated in this model. We conduct classification experiments on a publicly available BCI competition dataset for recognizing low, medium, and high cognitive workload levels. The experimental results demonstrate that our proposed BCJDA model based on cross-gradient difference maximization achieves the best performance.

Facial Action Unit Representation Based on Self-Supervised Learning With Ensembled Priori Constraints.

Facial action units (AUs) focus on a comprehensive set of atomic facial muscle movements for human expression understanding. Based on supervised learning, discriminative AU representation can be achieved from local patches where the AUs are located. Unfortunately, accurate AU localization and characterization are challenged by the tremendous manual annotations, which limits the performance of AU recognition in realistic scenarios. In this study, we propose an end-to-end self-supervised AU representation learning model (SsupAU) to learn AU representations from unlabeled facial videos. Specifically, the input face is decomposed into six components using auto-encoders: five photo-geometric meaningful components, together with 2D flow field AUs. By constructing the canonical neutral face, posed neutral face, and posed expressional face gradually, these components can be disentangled without supervision, therefore the AU representations can be learned. To construct the canonical neutral face without manually labeled ground truth of emotion state or AU intensity, two priori knowledge based assumptions are proposed: 1) identity consistency, which explores the identical albedos and depths of different frames in a face video, and helps to learn the camera color mode as an extra cue for canonical neutral face recovery. 2) average face, which enables the model to discover a 'neutral facial expression' of the canonical neutral face and decouple the AUs in representation learning. To the best of our knowledge, this is the first attempt to design self-supervised AU representation learning method based on the definition of AUs. Substantial experiments on benchmark datasets have demonstrated the superior performance of the proposed work in comparison to other state-of-the-art approaches, as well as an outstanding capability of decomposing input face into meaningful factors for its reconstruction. The code is made available at https://github.com/Sunner4nwpu/SsupAU.

Vision Based Gesture Recognition

The use of computers has become an integral part of our daily lives, and they are utilized in various fields. However, traditional input devices such as a mouse and keyboard can sometimes be limiting. Hand gestures can provide a more natural and efficient way of interacting with computers. However, since gestures can vary in orientation and shape from person to person, this creates a non-linear problem. Hence we propose an image-based gesture recognition method that leverages well-established models. The models were trained on a diverse dataset encompassing a wide range of gestures performed by various individuals under different environmental conditions. The experiments exhibit remarkable adaptability to different gestures resulting in a robust and accurate recognition in real-world scenarios. This research will help in creating better human-computer interaction interfaces offering users more natural and efficient ways to interact with computers. The improved gesture recognition systems elevate overall user experience and productivity.

A TinyML Model for Gesture-Based Air Handwriting Arabic Numbers Recognition

In an era where the demand for efficient and practical machine learning (ML) solutions on resource-constrained devices is evergrowing, the realm of tiny machine learning (TinyML) emerges as a promising frontier. Motivated by the need for lightweight, low-power models that can be deployed on edge devices, this research paper presents an innovative TinyML model tailored to recognize Arabic hand gestures executed in mid-air. With a primary emphasis on the precise classification of Arabic numbers through these expressive hand movements, the paper unveils a comprehensive dataflow architecture. This intricate architecture processes accelerometer and gyroscope data to derive exact 2D gesture coordinates, a fundamental component of the recognition process. The cornerstone of the proposed model is the integration of Convolutional Neural Networks (CNNs), elucidating their exceptional role in achieving an impressive 93.8% accuracy rate in the classification of diverse Arabic Numbers gestures. This remarkable level of precision underscores the model's efficacy and resilience, rendering it an ideal candidate for real-time deployment in various gesture recognition scenarios.

A False Sense of Privacy: Towards a Reliable Evaluation Methodology for the Anonymization of Biometric Data

Biometric data contains distinctive human traits such as facial features or gait patterns. The use of biometric data permits an individuation so exact that the data is utilized effectively in identification and authentication systems. But for this same reason, privacy protections become indispensably necessary. Privacy protection is extensively afforded by the technique of anonymization. Anonymization techniques protect sensitive personal data from biometrics by obfuscating or removing information that allows linking records to the generating individuals, to achieve high levels of anonymity. However, our understanding and possibility to develop effective anonymization relies, in equal parts, on the effectiveness of the methods employed to evaluate anonymization performance. In this paper, we assess the state-of-the-art methods used to evaluate the performance of anonymization techniques for facial images and for gait patterns. We demonstrate that the state-of-the-art evaluation methods have serious and frequent shortcomings. In particular, we find that the underlying assumptions of the state-of-the-art are quite unwarranted. State-of-the-art methods generally assume a difficult recognition scenario and thus a weak adversary. However, that assumption causes state-of-the-art evaluations to grossly overestimate the performance of the anonymization. Therefore, we propose a strong adversary which is aware of the anonymization in place. This adversary model implements an appropriate measure of anonymization performance. We improve the selection process for the evaluation dataset, and we reduce the numbers of identities contained in the dataset while ensuring that these identities remain easily distinguishable from one another. Our novel evaluation methodology surpasses the state-of-the-art because we measure worst-case performance and so deliver a highly reliable evaluation of biometric anonymization techniques.

Curved Geometric Networks for Visual Anomaly Recognition.

Learning a latent embedding to understand the underlying nature of data distribution is often formulated in Euclidean spaces with zero curvature. However, the success of the geometry constraints, posed in the embedding space, indicates that curved spaces might encode more structural information, leading to better discriminative power and hence richer representations. In this work, we investigate the benefits of the curved space for analyzing anomalous, open-set, or out-of-distribution (OOD) objects in data. This is achieved by considering embeddings via three geometry constraints, namely, spherical geometry (with positive curvature), hyperbolic geometry (with negative curvature), or mixed geometry (with both positive and negative curvatures). Three geometric constraints can be chosen interchangeably in a unified design, given the task at hand. Tailored for the embeddings in the curved space, we also formulate functions to compute the anomaly score. Two types of geometric modules (i.e., geometric-in-one (GiO) and geometric-in-two (GiT) models) are proposed to plug in the original Euclidean classifier, and anomaly scores are computed from the curved embeddings. We evaluate the resulting designs under a diverse set of visual recognition scenarios, including image detection (multiclass OOD detection and one-class anomaly detection) and segmentation (multiclass anomaly segmentation and one-class anomaly segmentation). The empirical results show the effectiveness of our proposal through consistent improvement over various scenarios. The code is made available at https://github.com/JHome1/GiO-GiT.

Fidgety Speech Emotion Recognition for Learning Process Modeling

In this paper, the recognition of fidgety speech emotion is studied, and real-world speech emotions are collected to enhance emotion recognition in practical scenarios, especially for cognitive tasks. We first focused on eliciting fidgety emotions and data acquisition for general math learning. Students practice mathematics by performing operations, solving problems, and orally responding to questions, all of which are recorded as audio data. Subsequently, the teacher evaluates the accuracy of these mathematical exercises by scoring, which reflects the cognitive outcomes of the students. Secondly, we propose an end-to-end speech emotion model based on a multi-scale one-dimensional (1-D) residual convolutional neural network. Finally, we conducted an experiment to recognize fidgety speech emotions by testing various classifiers, including SVM, LSTM, 1-D CNN, and the proposed multi-scale 1-D CNN. The experimental results show that the classifier we constructed can identify fidgety emotion well. After conducting a thorough analysis of fidgety emotions and their influence on the learning process, a clear relationship between the two was apparent. The automatic recognition of fidgety emotions is valuable for assisting on-line math teaching.

UniFi

In recent years, considerable endeavors have been devoted to exploring Wi-Fi-based sensing technologies by modeling the intricate mapping between received signals and corresponding human activities. However, the inherent complexity of Wi-Fi signals poses significant challenges for practical applications due to their pronounced susceptibility to deployment environments. To address this challenge, we delve into the distinctive characteristics of Wi-Fi signals and distill three pivotal factors that can be leveraged to enhance generalization capabilities of deep learning-based Wi-Fi sensing models: 1) effectively capture valuable input to mitigate the adverse impact of noisy measurements; 2) adaptively fuse complementary information from multiple Wi-Fi devices to boost the distinguishability of signal patterns associated with different activities; 3) extract generalizable features that can overcome the inconsistent representations of activities under different environmental conditions (e.g., locations, orientations). Leveraging these insights, we design a novel and unified sensing framework based on Wi-Fi signals, dubbed UniFi, and use gesture recognition as an application to demonstrate its effectiveness. UniFi achieves robust and generalizable gesture recognition in real-world scenarios by extracting discriminative and consistent features unrelated to environmental factors from pre-denoised signals collected by multiple transceivers. To achieve this, we first introduce an effective signal preprocessing approach that captures the applicable input data from noisy received signals for the deep learning model. Second, we propose a multi-view deep network based on spatio-temporal cross-view attention that integrates multi-carrier and multi-device signals to extract distinguishable information. Finally, we present the mutual information maximization as a regularizer to learn environment-invariant representations via contrastive loss without requiring access to any signals from unseen environments for practical adaptation. Extensive experiments on the Widar 3.0 dataset demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in different settings (99% and 90%-98% accuracy for in-domain and cross-domain recognition without additional data collection and model training).

Personalized Federated Learning Based on Bidirectional Knowledge Distillation for WiFi Gesture Recognition

WiFi-based human gesture recognition has a wide range of applications in smart homes. Existing methods train gesture classification models by collecting large amounts of WiFi signal data in a centralized manner. However, centralized training faces challenges, including high communication overhead and the risk of data privacy leakage. Federated learning (FL) provides an opportunity to collaboratively train and share models without compromising data privacy. One of the main challenges FL faces is data that is non-Independent and Identically Distributed (non-IID) across clients. Specifically, in the gesture recognition scenario, since the transmission of WiFi signals is susceptible to cross-environment and cross-person interference, non-IID mainly manifests itself as a cross-domain problem. Cross-domain makes the knowledge learned between client models incompatible. Therefore, in the cross-domain scenario, effectively extracting and combining the knowledge learned by the client is a challenge. To solve this problem, we propose pFedBKD, a novel personalized federated learning scheme via bidirectional distillation. First, the knowledge that is beneficial to the client is extracted from the shared server model through knowledge distillation in the client, which helps train the personalized model of the client. Second, the server adaptively adjusts the aggregation weights according to the deviation between the shared model and the client’s local model so that the server’s shared model can extract more public knowledge. We conduct experiments on multiple open-source datasets. Experimental results show that our method is superior to existing methods and effectively alleviates the problem of reduced model recognition accuracy caused by cross-domain challenges.

Sniffing Like a Wine Taster: Multiple Overlapping Sniffs (MOSS) Strategy Enhances Electronic Nose Odor Recognition Capability.

As highly promising devices for odor recognition, current electronic noses are still not comparable to human olfaction due to the significant disparity in the number of gas sensors versus human olfactory receptors. Inspired by the sniffing skills of wine tasters to achieve better odor perception, a multiple overlapping sniffs (MOSS) strategy is proposed in this study. The MOSS strategy involves rapid and continuous inhalation of odorants to stimulate the sensor array to generate feature-rich temporal signals. Computational fluid dynamics simulations are performed to reveal the mechanism of complex dynamic flows affecting transient responses. The proposed strategy shows over 95% accuracy in the recognition experiments of three gaseous alkanes and six liquors. Results demonstrate that the MOSS strategy can accurately and easily recognize odors with a limited sensor number. The proposed strategy has potential applications in various odor recognition scenarios, such as medical diagnosis, food quality assessment, and environmentalsurveillance.

Comparative Analysis VGG16 Vs MobileNet Performance for Fish Identification

This research aims to conduct a comparative evaluation of the efficacy of two neural network architectures in the field of fish identification through the utilization of supervised learning techniques. The evaluation of VGG16 and MobileNet, which are prominent deep learning architectures, has been conducted about their speed, accuracy, and efficiency in resource utilization. To assess the classification performance of both architectures, we employed a dataset encompassing diverse fish categories. The findings indicated that the VGG16 model demonstrated superior accuracy in fish classification, albeit due to increased computational time and resource utilization. On the contrary, MobileNet exhibits enhanced speed and efficiency, albeit at a marginal cost to its accuracy. The findings of this study have the potential to inform the selection of deep learning models for fish recognition scenarios, considering the specific requirements of the task, such as prioritizing accuracy or efficiency. The findings mentioned above offer significant insights that can be utilized in the advancement of Artificial Intelligence (AI)-based applications within the domains of fisheries resource management and environmental monitoring. These applications specifically necessitate precise and effective fish recognition capabilities. The comparison findings indicate that the accuracy achieved by VGG16 was 0.99, whereas MobileNet also attained an accuracy of 0.99.

EEG Topography Amplification Using FastGAN-ASP Method

Electroencephalogram (EEG) signals are bioelectrical activities generated by the central nervous system. As a unique information factor, they are correlated with the genetic information of the subjects, exhibiting robustness against forgery. The development of biometric identity recognition based on EEG signals has significantly improved the security and accuracy of biometric recognition. However, EEG signals obtained from incompatible acquisition devices have low universality and are prone to noise, making them challenging for direct use in practical identity recognition scenarios. Employing deep learning network models for data augmentation can address the issue of data scarcity. Yet, the time–frequency–space characteristics of EEG signals pose challenges for extracting features and efficiently generating data with deep learning models. To tackle these challenges, this paper proposes a data generation method based on channel attention normalization and spatial pyramid in a generative adversative network (FastGAN-ASP). The method introduces attention mechanisms in both the generator and discriminator to locate crucial feature information, enhancing the training performance of the generative model for EEG data augmentation. The EEG data used here are preprocessed EEG topographic maps, effectively representing the spatial characteristics of EEG data. Experiments were conducted using the BCI Competition IV-Ⅰ and BCI Competition IV-2b standard datasets. Quantitative and usability evaluations were performed using the Fréchet inception distance (FID) metric and ResNet-18 classification network, validating the quality and usability of the generated data from both theoretical and applied perspectives. The FID metric confirmed that FastGAN-ASP outperforms FastGAN, WGAN-GP, and WGAN-GP-ASP in terms of performance. Moreover, utilizing the dataset augmented with this method for classification recognition achieved an accuracy of 95.47% and 92.43%.

Enhancing Real-time Object Detection with YOLO Algorithm

This paper introduces YOLO, the best approach to object detection. Real-time detection plays a significant role in various domains like video surveillance, computer vision, autonomous driving and the operation of robots. YOLO algorithm has emerged as a well-liked and structured solution for real-time object detection due to its ability to detect items in one operation through the neural network. This research article seeks to lay out an extensive understanding of the defined Yolo algorithm, its architecture, and its impact on real-time object detection. This detection will be identified as a regression problem by frame object detection to spatially separated bounding boxes. Tasks like recognition, detection, localization, or finding widespread applicability in the best real-world scenarios, make object detection a crucial subdivision of computer vision. This algorithm detects objects in real-time using convolutional neural networks (CNN). Overall this research paper serves as a comprehensive guide to understanding the detection of objects in real-time using the You Only Look Once (YOLO) algorithm. By examining architecture, variations, and implementation details the reader can gain an understanding of YOLO’s capability.

On-road high-emitting vehicle identification by an automatic hyperparameter optimization model based on a remote sensing system

Optical remote sensing systems (RSSs) are ideal for monitoring and identifying high-emitting vehicles on roads, as they can be installed on any road for non-contact measurements. In general, an on-road vehicle is considered a high-emitting vehicle when its instantaneous emissions exceed the specified cut-points, as monitored by RSSs. However, RSS measurements of vehicle emissions are easily influenced by transient operating conditions of passing vehicles and multiple environmental factors, resulting in variable results for the same vehicle, further interfering with the screening of high-emitting vehicles. In this paper, an automatic hyperparametric optimization model is established in an RSS to identify high-emitting vehicles by fusing multi-feature data on environmental factors and vehicle operating conditions obtained from the RSS with the chassis and engine dynamometer test results provided by vehicle inspection stations (VISs). Qualitative and quantitative experimental results show that our model exhibits better recognition performance for high-emitting vehicles in RSSs of different times and sites, which reflects the good self-adaptability of the model. Moreover, the hyperparameters of the model do not need to be manually adjusted, so the model can be automatically trained to meet the requirements of real-time recognition scenarios for high-emitting vehicles on the road.

Design and Development of Traditional Elements in Visual Communication Design under the Integration of Virtual Reality Technology

Abstract This paper proposes a method based on visual communication to generate two-dimensional motion graphics that can enhance the perception of depth and three-dimensional structure in stereoscopic vision. Through eye tracking and calibration, gaze points in 3D space are calculated and applied to Chinese traditional elements in visual communication design. Feature vectors and improved plain Bayesian classifier methods are used to achieve user gesture recognition in virtual design and construct interaction design scenarios based on virtual reality technology. Lastly, the performance impact of augmented reality technology on traditional element design is investigated through a questionnaire survey. Based on the eight samples of conventional elements, the evaluation factors with the highest scores were found to be 10. In the subjective assessment of visual communication design, traditional components fall between mediocre and good, according to the ratings of all 18 assessment factors, which range from 0.2 to 1. The study presented in this paper effectively integrates virtual reality technology into the graphical design of traditional elements, significantly enhancing their visual expression.

Biometric Analysis on Smart Textile Garment in Real Life Scenario

The rapid proliferation of wearable applications and technologies capable of acquiring biomedical signals has prompted the incorporation of biomedical signals, such as the electrocardiogram (ECG), for biometric purposes in wearable platforms. Most ECG biometric research utilises medical-grade sensors in clinical settings, which is unrealistic for wearable ECG-based biometric applications in the real world. Therefore, this research aims to examine the ECG biometric on smart textile garments in real life, collected from commercially available wearable Hexoskin Proshirt and HeartIn Fit shirts. ECG data were obtained from 22 participants who took part in this study. The raw ECG signal is initially pre-processed using noise-removal Butterworth filters in the time domain, followed by an effective QRS segmented feature extraction technique. Finally, around 2076 datasets were created for training and validation, while the remaining 501 datasets were employed to test the suggested recognition approach with 29 Machine Learning Classifiers. Subsequently, Quadratic SVM has the highest accuracy at 96.8% for ECG biometrics, followed by Narrow Neural Network with 95.8% and Wide Neural Network with 95.4%. Further improvement to the QSVM parameter improved the accuracy to 97.4% with an error rate of 2.6%, followed by a sensitivity of 97.4% with a precision of 97.7% and a false rejection rate of 2.6%. Thus, the results of this study further validate the feasibility of applying ECG biometrics for recognition in real-life scenarios utilising a smart textile shirt with different configurations and brand is possible.