With the widespread deployment of biometric authentication in Internet of Things (IoT) applications, protecting biometric templates while maintaining recognition performance and low resource consumption has become an important issue. However, existing revocable biometric template protection methods are mainly based on random projection, whose linear structure poses a reversibility risk when both transformation parameters and multiple protected templates are compromised. To address this issue, this paper proposes a revocable biometric template generation method, termed Weighted Absolute Value Transformation based on Random Bezier Matrices (WAVTRBM). The proposed method employs random Bezier matrices to preserve the discriminative information of biometric features during transformation, and introduces a random weight vector into the absolute value transformation to improve similarity preservation and matching performance. The method can generate both real-valued and binary protected templates, with the advantages of low storage overhead and fast computation, making it suitable for resource-constrained IoT devices and real-time authentication scenarios. Experiments on face, fingerprint, palmprint, and palm vein databases show that, compared with conventional random projection-based methods and existing absolute value transformation-based methods, the proposed method improves template security while maintaining good recognition performance and supporting efficient template generation and matching. Theoretical and experimental analyses further demonstrate that the proposed method satisfies the requirements of revocable biometrics and can resist various attacks.

A novel reinforced dual-flow neural network based on attention and a polynomial-based radial basis function network (DFBTP) is proposed to enhance classification performance on tabular data. DFBTP consists of two types of architectures such as advanced Transformer model based on Tabular Prior data Fitted Network (TabPFN) and SV-clustering driven radial basis function neural network (SV-PRBFNN). The conventional PRBFNN model may encounter local minima and noise issues during training, which negatively impacts its performance. Local minima result from initial parameter choices, and noise issues are inherent in the dataset. By introducing the Transformer model and the Whale Optimization Algorithm (WOA), these challenges can be mitigated. Within the dual-flow architecture, the attention flow is trained using Bayesian inference capabilities and structural causal models, and it uses the self-attention mechanism to capture global features. This approach mitigates the problem of local minima. SV-PRBFNN flow uses the fuzzy clustering algorithm based on support vectors to replace the original radial basis function for training. Fuzzy clustering based on support vectors can alleviate the negative impact of outliers on model performance and also reduce the number of fuzzy rules. During neural network hyperparameter optimization, WOA is used to identify the global optimal values for hyperparameters. In the experiments, DFBTP demonstrated its superiority in classification accuracy in comparative experiments on 18 datasets and 13 models, and also performed well on real-world datasets. The robust performance of DFBTP was further validated through statistical analysis of the experimental results.

Existing partial fingerprint matching methods use fingerprint ridge features and minutiae or employ algorithms like SIFT and A-KAZA to create new feature points that can replace minutiae for feature extraction and matching. While these methods have achieved some success in improving matching performance, they rely on manually designed rules for extracting local area features, which limits their accuracy and generalization capability. To address these limitations, this paper proposes a novel partial fingerprint matching algorithm that leverages Feature Similarity and Pre-training. Specifically, Feature Similarity is integrated into a deep learning-based model to emulate traditional partial fingerprint matching techniques. Additionally, Pre-training guides the model to learn subtle yet identity-discriminative features within partial fingerprints. Experimental results on partial fingerprint databases constructed from FVC2004 DB1, DB2, and DB3 show that our algorithm achieves low EER and ZeroFMR, outperforming several state-of-the-art matching methods.

With the widespread application of biometric, unprotected biometric data is still at risk of serious security and privacy breaches. When large amounts of unprotected biometric data leak, cancelable biometric become a powerfully remedial measure. In this paper, we propose a new method to generate stable and cancelable face templates based on multiple partial Walsh transformations (MPWT) and Simhash. Firstly, multiple partial Walsh matrices generated with random external parameters perform projection transformation on the original real-valued face features, ensuring the irreversibility and unlinkability of the system. Subsequently, the projected features are transformed into discrete binary codes (protected templates) using Simhash. And the random permutation seed ensures the revocability of generated protected template. Furtherly, the protected templates have small storage space and is more suitable for fast comparison but also yields improvements in recognition accuracy compared with several state-of-the-arts. Numerous experiments on CASIA-WebFace, LFW, FEI, and Color FERET databases show that the protected templates are nearly identical to the unprotected ones in the comparison performance. The scheme also meets the requirements of non-invertibility, revocability, unlinkability, as well as resistance for various types of attacks like attacks via record multiplicity, false accepts, brute force and pre-image. Therefore, the proposed methodology strikes a balance between recognition accuracy and security.

The prefabricated concrete buildings (PCBs）are the booster in the process of construction industrialization and intelligent upgrading. However, its high cost has become one of the restricting factors of further application and promotion of prefabricated concrete buildings. Moreover, the existing investment estimation methods of prefabricated concrete buildings have limited predicting accuracy as well as the ability of adapting dynamic factors. Therefore, to achieve more reliable and reasonable investment estimation of prefabricated concrete buildings, this paper has proposed an investment estimation model of prefabricated concrete buildings based on XGBoost machine learning algorithm. In the proposed model, the construction project cost-significance theory (CS) and analytic hierarchy process (AHP) were used to extract the construction characteristic indices of prefabricated concrete buildings investment estimation. Then the XGBoost machine learning algorithm was implemented to build an investment estimation model of prefabricated concrete buildings that was able to quantify the uncertainty of the confidence and prediction, and to enhance the interpretability of the model. The research conducted in this paper showed that when compared with traditional machine learning methods such as Support vector machine (SVM), Back Propagation Neural Network (BPNN) and Random Forest (RF), XGBoost had better generalization and interpretable ability. The discussion provided in this paper further demonstrated the reliability and feasibility of the proposed model, and provided reliable basis for the investment decision-making of prefabricated concrete building projects.

Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention mechanism. This method combines the residual networks (ResNets) or the residual network of residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR networks to extract local features of age-sensitive regions, which effectively improves the age estimation accuracy. First, a ResNets or a RoR model pretrained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images. To extract the local features of age-sensitive regions, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically. Finally, the age group classification is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and 15/16LAP datasets. By combining the global and the local features, we obtain our final prediction results. Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network (CNN) methods on the Adience, MORPH Album 2, FG-NET and 15/16LAP datasets.

Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision which has a wide range of practical application value. Concerning the problem that accuracy of age estimation of face images under unconstrained conditions are relatively low for existing methods, we propose a method based on Attention LSTM network for Fine-Grained age group classification in the wild based on the idea of Fine-Grained categories and visual attention. This method combines ResNets models with LSTM unit to construct AL-ResNets networks to extract age-sensitive local regions, which effectively improves age estimation accuracy. Firstly, ResNets model pre-trained on ImageNet data set is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 data set for age estimation. Then, we fine-tune ResNets on the Adience data set to extract the global features of face images. To extract the local characteristics of age-sensitive areas, the LSTM unit is then presented to obtain the coordinates of the age-sensitive region automatically. Finally, by combining the global and local features, we got our final prediction results. Our experiments illustrate the effectiveness of AL-ResNets for age group classification in the wild, where it achieves new state-of-the-art performance than all other CNN methods on the Adience data set.

Automatically predicting age group from face images acquired in unconstrained conditions is an important and challenging task in many real-world applications. Nevertheless, the conventional methods with manually-designed features on in-the-wild benchmarks are unsatisfactory because of incompetency to tackle large variations in unconstrained images. In this paper, we propose a new CNN based method for age group classification leveraging Residual Networks of Residual Networks (RoR), which exhibits better optimization ability for age group classification than other CNN architectures. Moreover, two modest mechanisms based on observation of the characteristics of age group are presented to further improve the performance of age estimation. Our experiments illustrate the effectiveness of RoR method for age estimation in the wild, where it achieves better performance than other CNN methods. Finally, the Pre-RoR-58+SD with two mechanisms achieves new state-of-the-art results on Adience benchmark.

The rapid development of mobile games highlights the power consumption problem in the mobile platform. Most of the power saving techniques use the prediction-based dynamic voltage frequency scaling (DVFS) scheme. However, the prediction could be inaccurate resulting from the frequent interactions of user when playing games. We have observed that frame rate is near-linear to CPU frequency, but there is a bottleneck, frame rate will not increase as CPU frequency increases when CPU frequency reaches this threshold. Moreover, previous research has shown that utilizing the information of game state can reduce the influence of game interactive characterization to DVFS policy. We explore a method to automatically detect the game state. We propose the Automatic Frame Rate-Based DVFS policy, which can learn the threshold of frame rate online and utilize the information of game state and frame rate to scale the frequency without prediction. Our evaluation result shows that, compared with the prediction-based Android default Interactive DVFS policy, our policy saves more power in all the testing games. Up to 15.2% more power can be saved by Automatic Frame Rate-Based DVFS policy.

Affine-invariant feature matching plays an important role in many robot vision applications, such as robot visual navigation, object detection, visual tracking and visual SLAM, etc. In the early stages, invariant keypoints are used to detect the affine transformation. But the accuracy is very low. In recent years, some people introduce SIFT method into robot vision field, which greatly enhances the accuracy. But it is too time-consuming to meet the requirements of real-time robot vision applications. In this paper, we propose a novel learning-based feature matching approach to address the problem. First, it uses a fast algorithm to extract keypoints. Then, our method identifies keypoints that belong to different objects or background by color and texture representation. The keypoints are clustered into corresponding groups. At last, a two-stage multilayer ferns classifier is trained to recognize the local patches and get the estimate of viewpoint. We test our approach on public datasets and apply it in a visual SLAM application. The result demonstrates that our method can provide robust and powerful matching ability. Even on some difficult matching cases, it also performs remarkably well. Further more, because there is no need to compute descriptors for the image, our method is very fast at run-time.

While local patches recognition is a key component of modern approaches to affine transformation detection and object detection, existing learning-based approaches just identify the patches based on a set of randomly picked and combined binary features, which will lose some strong correlations between features and can not provide stable and remarkable identification ability. In this paper, we proposed a method that select and organize the features in a Multilayer Ferns structure, and show that it is both faster in the run-time processing and more powerful in the identification ability than state-of-the-art ad hoc approaches.