Jin Su Kim, Sungbum Pan^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1663-1678, 2023, DOI:10.32604/cmc.2023.043998

Abstract As Internet of Things (IoT) technology develops, integrating network functions into diverse equipment introduces new challenges, particularly in dealing with counterfeit issues. Over the past few decades, research efforts have focused on leveraging electromyogram (EMG) for personal recognition, aiming to address security concerns. However, obtaining consistent EMG signals from the same individual is inherently challenging, resulting in data irregularity issues and consequently decreasing the accuracy of personal recognition. Notably, conventional studies in EMG-based personal recognition have overlooked the issue of data irregularities. This paper proposes an innovative approach to personal recognition that combines a siamese fusion network with an auxiliary… More >

Harsh Mankodiya¹, Priyal Palkhiwala¹, Rajesh Gupta^1,*, Nilesh Kumar Jadav¹, Sudeep Tanwar¹, Osama Alfarraj², Amr Tolba², Maria Simona Raboaca^3,4,*, Verdes Marina⁵

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1123-1142, 2023, DOI:10.32604/cmc.2023.038556

Abstract The amalgamation of artificial intelligence (AI) with various areas has been in the picture for the past few years. AI has enhanced the functioning of several services, such as accomplishing better budgets, automating multiple tasks, and data-driven decision-making. Conducting hassle-free polling has been one of them. However, at the onset of the coronavirus in 2020, almost all worldly affairs occurred online, and many sectors switched to digital mode. This allows attackers to find security loopholes in digital systems and exploit them for their lucrative business. This paper proposes a three-layered deep learning (DL)-based authentication framework to develop a secure online… More >

Zhenyu Huang^1,2, Gun Li², Xudong Sun¹, Yong Chen¹, Jie Sun¹, Zhangsong Ni^1,*, Yang Yang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3219-3238, 2023, DOI:10.32604/cmc.2023.039489

Abstract Onboard visual object tracking in unmanned aerial vehicles (UAVs) has attracted much interest due to its versatility. Meanwhile, due to high precision, Siamese networks are becoming hot spots in visual object tracking. However, most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs. To meet the tracking precision and real-time requirements, this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL. Specifically, the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network, then performs correlation matching to obtain the candidate… More >

T. Satyanarayana Murthy¹, Kollati Vijaya Kumar², Fayadh Alenezi³, E. Laxmi Lydia⁴, Gi-Cheon Park⁵, Hyoung-Kyu Song⁶, Gyanendra Prasad Joshi⁷, Hyeonjoon Moon^7,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1633-1650, 2023, DOI:10.32604/csse.2023.034769

Abstract Fruit classification utilizing a deep convolutional neural network (CNN) is the most promising application in personal computer vision (CV). Profound learning-related characterization made it possible to recognize fruits from pictures. But, due to the similarity and complexity, fruit recognition becomes an issue for the stacked fruits on a weighing scale. Recently, Machine Learning (ML) methods have been used in fruit farming and agriculture and brought great convenience to human life. An automated system related to ML could perform the fruit classifier and sorting tasks previously managed by human experts. CNN’s (convolutional neural networks) have attained incredible outcomes in image classifiers… More >

Mohamed Ezz, Waad Alanazi, Ayman Mohamed Mostafa^*, Eslam Hamouda, Murtada K. Elbashir, Meshrif Alruily

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2299-2317, 2023, DOI:10.32604/csse.2023.036567

Abstract Palmprint identification has been conducted over the last two decades in many biometric systems. High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues. This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication. The proposed model has two stages of learning; the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model. The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity. The proposed model achieves robust and reliable end-to-end Palmprint… More >

Yannan Cai, Ke Tan, Zhenzhong Wei^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1621-1640, 2023, DOI:10.32604/cmc.2023.036177

Abstract Label assignment refers to determining positive/negative labels for each sample to supervise the training process. Existing Siamese-based trackers primarily use fixed label assignment strategies according to human prior knowledge; thus, they can be sensitive to predefined hyperparameters and fail to fit the spatial and scale variations of samples. In this study, we first develop a novel dynamic label assignment (DLA) module to handle the diverse data distributions and adaptively distinguish the foreground from the background based on the statistical characteristics of the target in visual object tracking. The core of DLA module is a two-step selection mechanism. The first step… More >

Eslam Hamouda¹, Mohamed Ezz^1,*, Ayman Mohamed Mostafa¹, Murtada K. Elbashir¹, Meshrif Alruily¹, Mayada Tarek²

Computer Systems Science and Engineering, Vol.46, No.1, pp. 619-636, 2023, DOI:10.32604/csse.2023.035830

Abstract Many types of research focus on utilizing Palmprint recognition in user identification and authentication. The Palmprint is one of biometric authentication (something you are) invariable during a person’s life and needs careful protection during enrollment into different biometric authentication systems. Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification. This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication. A HAMTE-Siamese network is constructed, which accepts two Palmprint templates and predicts whether these two templates belong to the… More >

Nagwa Elaraby^*, Sherif Barakat, Amira Rezk

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1837-1854, 2023, DOI:10.32604/cmc.2023.032288

Abstract Deep metric learning is one of the recommended methods for the challenge of supporting few/zero-shot learning by deep networks. It depends on building a Siamese architecture of two homogeneous Convolutional Neural Networks (CNNs) for learning a distance function that can map input data from the input space to the feature space. Instead of determining the class of each sample, the Siamese architecture deals with the existence of a few training samples by deciding if the samples share the same class identity or not. The traditional structure for the Siamese architecture was built by forming two CNNs from scratch with randomly… More >

Zhangdong Wang¹, Jiaohua Qin^1,*, Xuyu Xiang¹, Yun Tan¹, Neal N. Xiong²

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 2931-2946, 2022, DOI:10.32604/cmc.2022.028896

Abstract Visual object tracking is a hot topic in recent years. In the meanwhile, Siamese networks have attracted extensive attention in this field because of its balanced precision and speed. However, most of the Siamese network methods can only distinguish foreground from the non-semantic background. The fine-tuning and retraining of fully-convolutional Siamese networks for object tracking(SiamFC) can achieve higher precision under interferences, but the tracking accuracy is still not ideal, especially in the environment with more target interferences, dim light, and shadows. In this paper, we propose criss-cross attentional Siamese networks for object tracking (SiamCC). To solve the imbalance between foreground… More >

Shaozhe Guo¹, Yong Li^1,*, Xuyang Chen², Youshan Zhang¹

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3137-3148, 2022, DOI:10.32604/cmc.2022.026784

Abstract The Visual tracking problem can usually be solved in two parts. The first part is to extract the feature of the target and get the candidate region. The second part is to realize the classification of the target and the regression of the bounding box. In recent years, Siameses network in visual tracking problem has always been a frontier research hotspot. In this work, it applies two branches namely search area and tracking template area for similar learning to track. Some related researches prove the feasibility of this network structure. According to the characteristics of two branch shared networks in… More >