Implicit Neural Representations (INRs) have become a powerful paradigm for modeling continuous signals in computer vision, graphics, and scientific computing. However, multilayer perceptrons (MLPs) generally suffer from severe spectral bias, which limits their ability to accurately model high-frequency details and multi-scale structures. To address this challenge, we propose a novel Multi-Scale Sine Activation (MSA), which explicitly introduces multi-scale frequency responses by incorporating multiple sets of sine activations with logarithmically spaced frequencies in parallel at each layer. MSA is further combined with an amplitude modulation mechanism to ensure numerical stability and robust optimization across different frequency channels. We conduct extensive experiments on a series of challenging tasks, including 1D multi-scale function fitting, image representation, video representation, 3D shape representation, and PDEs solving. Experimental results show that MSA outperforms existing state-of-the-art methods in terms of reconstruction accuracy, detail preservation, and training stability.

With the accumulation of resources in the era of Big Data and the rise of pre-trained models in deep learning, optimizing neural networks for various tasks often involves different strategies for fine-tuning pre-trained models versus training from scratch. However, existing optimizers primarily focus on reducing the loss function by updating model parameters, without fully addressing the unique demands of these two major paradigms. In this paper, we propose DualOpt, a novel approach that decouples optimization techniques specifically tailored for these distinct training scenarios. For training from scratch, we introduce real-time layer-wise weight decay, designed to enhance both convergence and generalization by aligning with the characteristics of weight updates and network architecture. For more importantly fine-tuning, we integrate weight rollback with the optimizer, incorporating a rollback term into each weight update step. This ensures consistency in the weight distribution between upstream and downstream models, effectively mitigating knowledge forgetting and improving fine-tuning performance. Additionally, we extend the layer-wise weight decay to dynamically adjust the rollback levels across layers, adapting to the varying demands of different downstream tasks. Extensive experiments across diverse tasks, including image classification, object detection, semantic segmentation, and instance segmentation, demonstrate the broad applicability and state-of-the-art performance of DualOpt.

Mathematical formulas are the language of communication between humans and nature. Discovering latent formulas from observed data is an important challenge in artificial intelligence, commonly known as symbolic regression(SR). The current mainstream SR algorithms regard SR as a combinatorial optimization problem and use Genetic Programming (GP) or Reinforcement Learning (RL) to solve the SR problem. These methods perform well on simple problems, but poorly on slightly more complex tasks. In addition, this class of algorithms ignores an important aspect: in SR tasks, symbols have explicit numerical meaning. So can we take full advantage of this important property and try to solve the SR problem with more efficient numerical optimization methods? Extrapolation and Learning Equation (EQL) replaces activation functions in neural networks with basic symbols and sparsifies connections to derive a simplified expression from a large network. However, EQL's fixed network structure can't adapt to the complexity of different tasks, often resulting in redundancy or insufficient, limiting its effectiveness. Based on the above analysis, we propose MetaSymNet, a tree-like network that employs the PANGU meta-function as its activation function. PANGU meta-function can evolve into various candidate functions during training. The network structure can also be adaptively adjusted according to different tasks. Then the symbol network evolves into a concise, interpretable mathematical expression. To evaluate the performance of MetaSymNet and five baseline algorithms, we conducted experiments across more than ten datasets, including SRBench. The experimental results show that MetaSymNet has achieved relatively excellent results on various evaluation metrics.

While existing pre-training-based methods have enhanced point cloud model performance, they have not fundamentally resolved the challenge of local structure representation in point clouds. The limited representational capacity of pure point cloud models continues to constrain the potential of cross-modal fusion methods and performance across various tasks. To address this challenge, we propose a Dynamic Acoustic Field Fitting Network (DAF-Net), inspired by physical acoustic principles. Specifically, we represent local point clouds as acoustic fields and introduce a novel Acoustic Field Convolution (AF-Conv), which treats local aggregation as an acoustic energy field modeling problem and captures fine-grained local shape awareness by dividing the local area into near field and far field. Furthermore, drawing inspiration from multi-frequency wave phenomena and dynamic convolution, we develop the Dynamic Acoustic Field Convolution (DAF-Conv) based on AF-Conv. DAF-Conv dynamically generates multiple weights based on local geometric priors, effectively enhancing adaptability to diverse geometric features. Additionally, we design a Global Shape-Aware (GSA) layer incorporating EdgeConv and multi-head attention mechanisms, which combines with DAF-Conv to form the DAF Block. These blocks are then stacked to create a hierarchical DAFNet architecture. Extensive experiments demonstrate that DAFNet significantly outperforms existing methods across multiple tasks.

The quest for analytical solutions to differential equations has traditionally been constrained by the need for extensive mathematical expertise. Machine learning methods like genetic algorithms have shown promise in this domain, but are hindered by significant computational time and the complexity of their derived solutions. This paper introduces **SSDE** (Symbolic Solver for Differential Equations), a novel reinforcement learning-based approach that derives symbolic closed-form solutions for various differential equations. Evaluations across a diverse set of ordinary and partial differential equations demonstrate that SSDE outperforms existing machine learning methods, delivering superior accuracy and efficiency in obtaining analytical solutions.

Few-shot class-incremental learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. Foundation models combined with prompt tuning showcase robust generalization and zero-shot learning (ZSL) capabilities, endowing them with potential advantages in transfer capabilities for FSCIL. However, existing prompt tuning methods excel in optimizing for stationary datasets, diverging from the inherent sequential nature in the FSCIL paradigm. To address this issue, taking inspiration from the "fast and slow mechanism" of the complementary learning systems (CLSs) in the brain, we present fast- and slow-update prompt tuning FSCIL (FSPT-FSCIL), a brain-inspired prompt tuning method for transferring foundation models to the FSCIL task. We categorize the prompts into two groups: fast-update prompts and slow-update prompts, which are interactively trained through meta-learning. Fast-update prompts aim to learn new knowledge within a limited number of iterations, while slow-update prompts serve as meta-knowledge and aim to strike a balance between rapid learning and avoiding catastrophic forgetting. Through experiments on multiple benchmark tests, we demonstrate the effectiveness and superiority of FSPT-FSCIL. The code is available at https://github.com/qihangran/FSPT-FSCIL.

Symbolic regression (SR) is the process of finding an unknown mathematical expression given the input and output and has important applications in interpretable machine learning and knowledge discovery. The major difficulty of SR is that finding the expression structure is an NP-hard problem, which makes the entire process time-consuming. In this study, the solution of expression structures was regarded as a classification problem and solved by supervised learning such that SR can be solved quickly by using the solving experience. Techniques for classification tasks, such as equivalent label merging and sample balance, were used to enhance the robustness of the algorithm. We proposed a symbolic network called DeepSymNet to represent symbolic expressions to improve the performance of the algorithm. DeepSymNet has been proven to have a strong representation ability with a shorter label compared to the current popular representation methods, reducing the search space when predicting. Moreover, DeepSymNet conveniently decomposes SR into two smaller subproblems, which makes solving the problem easier. The proposed algorithm was tested on artificially generated expressions and public datasets and compared with other algorithms. The results demonstrate the effectiveness of the proposed algorithm.

Symbolic regression (SR) is a powerful technique for discovering the underlying mathematical expressions from observed data. Inspired by the success of deep learning, recent deep generative SR methods have shown promising results. However, these methods face difficulties in processing high-dimensional problems and learning constants due to the large search space, and they don’t scale well to unseen problems. In this work, we propose DySymNet, a novel neural-guided Dynamic Symbolic Network for SR. Instead of searching for expressions within a large search space, we explore symbolic networks with various structures, guided by reinforcement learning, and optimize them to identify expressions that better-fitting the data. Based on extensive numerical experiments on low-dimensional public standard benchmarks and the well-known SRBench with more variables, DySymNet shows clear superiority over several representative baseline models. Open source code is available at https://github.com/AILWQ/DySymNet.

With the development of artificial intelligence, computer vision technology has been widely used in the fields of security monitoring, automatic driving and wisdom city. However, there has not been a research on the detection of the meridians in human bodies by using the computer vision technology. In order to promote the use of the computer vision technology in human meridian detection, this paper first releases a dataset based on human meridians, which makes up for the gap in the field of human meridian detection using image processing technology. Moreover, the human meridian detection dataset is manually annotated and proofread by experienced Traditional Chinese Medicine (TCM) practitioners according to the position and direction of the human meridians, so that the annotated human meridians are as accurate as possible. The released human meridian dataset label’s 12 meridians, including spleen meridian, pericardium meridian, stomach meridian, lung meridian, heart meridian, kidney meridian, gallbladder meridian, liver meridian, triple energizer meridian, bladder meridian, large intestine meridian and small intestine meridian. A total of 296 acupoints were labeled. At last, this paper proposes a method for data augmentation, especially for datasets with a small amount of data, wherein the data amount can be augmented by enhancing the underlying edge visual features of the data. Experimental results show that human meridians can be detected by using image processing technology, and the proposed method for data augmentation can effectively improve the detection accuracy of human meridians. The dataset can be downloaded from https://www.zksylf.com/col.jsp?id=127 .

Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class margins based on empirical observations, which can be suboptimal. The emerging Neural Collapse (NC) theory provides a theoretically optimal inter-class margin for classification, serving as a basis for adaptively computing the margin. Yet, it is designed for closed, balanced data, not for sequential or few-shot imbalanced data. To address this gap, we propose a Meta-learning- and NC-based FSCIL method, MetaNC-FSCIL, to compute the optimal margin adaptively and maintain it at each incremental session. Specifically, we first compute the theoretically optimal margin based on the NC theory. Then we introduce a novel loss function to ensure that the loss value is minimized precisely when the inter-class margin reaches its theoretically best. Motivated by the intuition that “learn how to preserve the margin” matches the meta-learning’s goal of “learn how to learn”, we embed the loss function in base-session meta-training to preserve the margin for future meta-testing sessions. Experimental results demonstrate the effectiveness of MetaNC-FSCIL, achieving superior performance on multiple datasets. The code is available at https://github.com/qihangran/metaNC-FSCIL.

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.

An artistic line portrait robot can generate, process, and draw line portraits. Compared to real face images, line portraits lose some recognizable information. Maintaining recognizability during the process of expression edition of line portraits is an important challenge for artistic portrait robots. A recognizable expression line portrait synthesis method based on a triangle coordinate system (TCS) is proposed. First, based on public facial expression databases [JAFFE, Oulu CASIA, RaFD, and Cohn-Kanade (CK)], by studying the feature deviations between different expressions of the same person, an expression deformation constraint criterion (EDCC) that is conducive to maintaining recognizable features is proposed. Then, by comparing features between the source line portrait and reference expression portrait, the expression features are calculated. Finally, under the EDCC, based on expression features, a recognizable expression line portrait is generated through image topological deformation based on TCS. In addition, we can synthesize different degrees of expression line portraits. On the public face datasets (FHHQ, CelebA-HQ, and CK), we implemented qualitative and quantitative contrast experiments. Experimental results demonstrate that this method can automatically synthesize an expression line portrait with reference expression, where the expression degree of the reference expression is controllable, and the generated expression portrait still has high recognizability. The expression samples generated by the proposed method are used for face authentication on the CK dataset, and only 0.22% of the samples fail to pass the authentication.

Person re-identification (Re-ID) has played an extremely crucial role in ensuring social safety and has attracted considerable research attention. 3D shape information is an important clue to understand the posture and shape of pedestrians. However, most existing person Re-ID methods learn pedestrian feature representations from images, ignoring the real 3D human body structure and the spatial relationship between the pedestrians and interferents. To address this problem, our devise a new point cloud Re-ID network (PointReIDNet), designed to obtain 3D shape representations of pedestrians from point clouds of 3D scenes. The model consists of modules, namely global semantic guidance module and local feature extraction module. The global semantic guidance module is designed by enhancing the point cloud feature representation in similar feature neighborhoods and to reduce the interference caused by 3D shape reconstruction or noise. Further, to provide an efficient representation of point clouds, we propose space cover convolution (SC-Conv), which efficiently encodes information on human shapes in local point clouds by constructing anisotropic geometries in the coordinate neighborhoods. Extensive experiments are conducted on four holistic person Re-ID datasets, one occlusion person Re-ID dataset and one point cloud classification dataset. The results exhibit significant improvements over point-cloud-based person Re-ID methods. In particular, the proposed efficient PointReIDNet decreases the number of parameters from 2.30M to 0.35M with an insignificant drop in performance. The source code is available at: https://github.com/changshuowang/PointReIDNet.

Recent development in computing power has resulted in performance improvements on holistic(none-occluded) person Re-Identification (ReID) tasks. Nevertheless, the precision of the recent research will diminish when a pedestrian is obstructed by obstacles. Within the realm of 2D space, the loss of information from obstructed objects continues to pose significant challenges in the context of person ReID. Person is a 3D non-grid object, and thus semantic representation learning in only 2D space limits the understanding of occluded person. In the present work, we propose a network based on 3D multi-view learning, allowing it to acquire geometric and shape details of an occluded pedestrian from 3D space. Simultaneously, it capitalizes on advancements in 2D-based networks to extract semantic representations from 3D multi-views. Specifically, the surface random selection strategy is proposed to convert images of 2D RGB into 3D multi-views. Using this strategy, we build four extensive 3D multi-view data collections for person ReID. After that, Pedestrian 3D Shape Understanding for Person Re-Identification via Multi-View Learning(MV-3DSReID), is proposed for identifying the person by learning person geometry and structure representation from the groups of multi-view images. In comparison to alternative data formats (e.g., 2D RGB, 3D point cloud), multi-view images complement each other’s detailed features of the 3D object by adjusting rendering viewpoints, thus facilitating a more comprehensive understanding of the person for both holistic and occluded ReID situations. Experiments on occluded and holistic ReID tasks demonstrate performance levels comparable to state-of-the-art methods, validating the effectiveness of our proposed approach in tackling challenges related to occlusion. The code is available at https://github.com/hangjiaqi1/MV-TransReID.

Abstract Image quality assessment is to simulate subjective human visual perception and realize image quality inference automatically. Although deep neural networks have achieved great success, the majority of them do not fully consider perception characteristics. Therefore, according to the human visual scale characteristics, we proposed an image quality assessment algorithm based on multiscale and dual domains fusion. Firstly, the original image and its phase congruency respectively input into two branches, feature pyramid and channel attention mechanism are adopted to extract multiscale features. After that, bilinear pool is used to aggregate the spatial and frequency domain characteristics of the corresponding scales, and allows arbitrary scale input to ensure that the features are extracted from the inherent quality images. Finally, the single quality score is obtained through learned weights of each scale. Comparative experiments between our approach and state‐of‐the‐art are conducted on five public databases, the results demonstrate that the proposed algorithm is not only robust to different types and across database, but also sensitive to scale.

The incremental learning paradigm in machine learning has consistently been a focus of academic research. It is similar to the way in which biological systems learn, and reduces energy consumption by avoiding excessive retraining. Existing studies utilize the powerful feature extraction capabilities of pre-trained models to address incremental learning, but there remains a problem of insufficient utilization of neural network feature knowledge. To address this issue, this paper proposes a novel method called Pre-trained Model Knowledge Distillation (PMKD) which combines knowledge distillation of neural network representations and replay. This paper designs a loss function based on centered kernel alignment to transfer neural network representations knowledge from the pre-trained model to the incremental model layer-by-layer. Additionally, the use of memory buffer for Dark Experience Replay helps the model retain past knowledge better. Experiments show that PMKD achieved superior performance on various datasets and different buffer sizes. Compared to other methods, our class incremental learning accuracy reached the best performance. The open-source code is published at https://github.com/TianSongS/PMKD-IL .

Recently, deep neural networks (DNNs) promote mainly by network architectures and loss functions; however, the development of neuron models has been quite limited. In this study, inspired by the mechanism of human cognition, a hyper-sausage coverage function (HSCF) neuron model possessing a high flexible plasticity. Then, a novel cross-entropy and volume-coverage (CE_VC) loss is defined, which compresses the volume of the hyper-sausage to the hilt, and helps alleviate confusion among different classes, thus ensuring the intra-class compactness of the samples. Finally, a divisive iteration method is introduced, which considers each neuron model as a weak classifier, and iteratively increases the number of weak classifiers. Thus, the optimal number of the HSCF neuron is adaptively determined and an end-to-end learning framework is constructed. In particular, to improve the classification performance, the HSCF neuron can be applied to classical DNNs. Comprehensive experiments on eight datasets in several domains demonstrate the effectiveness of the proposed method. The proposed method exhibits the feasibility of boosting DNNs with neuron plasticity and provides a novel perspective for further developments in DNNs. The source code is available at https://github.com/Tough2011/HSCFNet.git .

Summary In recent years, with the rapid development of adversarial learning technology, facial attribute editing has made great success in a number of areas. Realistic visual effect, invariant identity information, and accurate editing area are the three key issues of facial attribute editing. Unfortunately, most researches focus on the former two problems. However, lack of awareness of the accurate editing area in the task is the main reason for damaging attribute‐irrelevant details. To address this issue, this article proposes a novel facial attribute editing algorithm—a generative adversarial network (GAN) with semantic masks—from the perspective of editing location accuracy. By generating the mask with respect to attribute‐related areas, the semantic segmentation network can only constrain the manipulation in the target region while not harming any attribute‐irrelevant details. The GAN is then combined with the semantic segmentation network to formulate the entire framework, which is referred to as SM‐GAN. Extensive experiments on the public datasets CelebA and LFWA prove that the presented method can not only ensure that the attribute manipulation is realistic, but also allow attribute‐irrelevant regions to remain unchanged. Moreover, it can also simultaneously edit multiple facial attributes.

Summary In recent years, finger vein recognition has attracted more attention and research as a secure method of identification. Convolutional neural networks have achieved great success in the field of finger vein recognition, yet they suffer from high computational complexity, large parameters, and other challenges. To solve these problems, we propose a Gabor convolutional neural network with receptive fields. We use Gabor filters with receptive field properties to design Gabor convolutional layers. Then we replace the conventional convolutional layer with the Gabor convolutional layer; analyze the influence of different loss functions, convolution kernel size, and feature size on the network model; and choose the most suitable model parameters and loss function. Finally, we systematically investigate comparative performance using AGCNN and CNNs in different finger vein databases. Experimental results show that the parameter complexity of AGCNN is significantly less than that of CNNs with a slight performance decrease.

At present, effectively aggregating and transferring the local features of point cloud is still an unresolved technological conundrum. In this study, we propose a new space-cover convolutional neural network (SC-CNN) for tasks such as point cloud classification and segmentation. The core of this network is space-cover convolution (SC-Conv), which implements depthwise separable convolution on the point cloud. In addition, a newly designed space-cover operator (SCOP) replaces depthwise convolution. The key to SC-Conv is constructing anisotropic spatial geometry in the local point cloud. The SCOP achieves this by utilizing the positional and feature relationships to learn the high-order relationship expression between points. First, data-driven adaptive learning from the 3-D coordinate relationship between the local points is used to determine the weight of the SCOP. Then, the edge feature of the neighboring point relative to the sampling point is used as the input of the SCOP. Finally, a deformable spatial geometry is constructed in the feature space between local points to aggregate the local high-order features. By stacking SC-Conv to construct SC-CNN with a hierarchical network structure for point cloud analysis, we can better perceive the shape information of point cloud and improve network robustness. Finally, we provide numerous experiments to verify that SC-CNN parallels or even outperforms advanced methods in shape classification, part segmentation, and large-scale indoor scene segmentation tasks. The open-source code was published athttps://github.com/changshuowang/SC-CNN.

Modeling, prediction, and recognition tasks depend on the proper representation of the objective curves and surfaces. Polynomial functions have been proved to be a powerful tool for representing curves and surfaces. Until now, various methods have been used for polynomial fitting. With a recent boom in neural networks, researchers have attempted to solve polynomial fitting by using this end-to-end model, which has a powerful fitting ability. However, the current neural network-based methods are poor in stability and slow in convergence speed. In this article, we develop a novel neural network-based method, called Encoder-X, for polynomial fitting, which can solve not only the explicit polynomial fitting but also the implicit polynomial fitting. The method regards polynomial coefficients as the feature value of raw data in a polynomial space expression and therefore polynomial fitting can be achieved by a special autoencoder. The entire model consists of an encoder defined by a neural network and a decoder defined by a polynomial mathematical expression. We input sampling points into an encoder to obtain polynomial coefficients and then input them into a decoder to output the predicted function value. The error between the predicted function value and the true function value can update parameters in the encoder. The results prove that this method is better than the compared methods in terms of stability, convergence, and accuracy. In addition, Encoder-X can be used for solving other mathematical modeling tasks.

In the task of person re-identification, the attention mechanism and fine-grained information have been proved to be effective. However, it has been observed that models often focus on the extraction of features with strong discrimination, and neglect other valuable features. The extracted fine-grained information may include redundancies. In addition, current methods lack an effective scheme to remove background interference. Therefore, this paper proposes the feature refinement and filter network to solve the above problems from three aspects: first, by weakening the high response features, we aim to identify highly valuable features and extract the complete features of persons, thereby enhancing the robustness of the model; second, by positioning and intercepting the high response areas of persons, we eliminate the interference arising from background information and strengthen the response of the model to the complete features of persons; finally, valuable fine-grained features are selected using a multi-branch attention network for person re-identification to enhance the performance of the model. Our extensive experiments on the benchmark Market-1501, DukeMTMC-reID, CUHK03 and MSMT17 person re-identification datasets demonstrate that the performance of our method is comparable to that of state-of-the-art approaches.

The generative adversarial network (GAN) exhibits great superiority in the face attribute synthesis task. However, existing methods have very limited effects on the expansion of new attributes. To overcome the limitations of a single network in new attribute synthesis, a continuous learning method for face attribute synthesis is proposed in this work. First, the feature vector of the input image is extracted and attribute direction regression is performed in the feature space to obtain the axes of different attributes. The feature vector is then linearly guided along the axis so that images with target attributes can be synthesized by the decoder. Finally, to make the network capable of continuous learning, the orthogonal direction modification module is used to extend the newly-added attributes. Experimental results show that the proposed method can endow a single network with the ability to learn attributes continuously, and, as compared to those produced by the current state-of-the-art methods, the synthetic attributes have higher accuracy.

Local feature descriptors exhibit great superiority in finger vein recognition due to their stability and robustness against local changes in images. However, most of these are methods use general-purpose descriptors that do not consider finger vein-specific features. In this work, we propose a finger vein-specific local feature descriptors based physiological characteristic of finger vein patterns, i.e., histogram of oriented physiological Gabor responses (HOPGR), for finger vein recognition. First, a prior of directional characteristic of finger vein patterns is obtained in an unsupervised manner. Then the physiological Gabor filter banks are set up based on the prior information to extract the physiological responses and orientation. Finally, to make the feature robust against local changes in images, a histogram is generated as output by dividing the image into non-overlapping cells and overlapping blocks. Extensive experimental results on several databases clearly demonstrate that the proposed method outperforms most current state-of-the-art finger vein recognition methods.

In the field of 3D face alignment, most researchers have focused on improving the prediction accuracy of algorithms and ignored the portability for practical applications. To this end, this study presents a real-time 3D face-alignment method that uses an encoder-decoder network with an efficient deconvolution layer. The fusion of the encoding and decoding feature adds more abundant features to this network. An efficient deconvolution layer at the decoding stage applies the L1 norm to select useful features and generate abundant ones through linear operations. Experimental results using the standard AFLW2000-3D and AFLW-LFPA datasets show that our algorithm has low prediction errors with real-time applicability.

Homology Continuity is a fundamental property of the nature, but few of the traditional pattern recognition algorithms were aware of it. Firstly, this paper gives a brief description to the Principle of Homology Continuity (PHC), and tries to mathematically redefine it. Then, we introduce a PHC-based pattern learning method — Geometrical Covering Learning (GCL), following the Hyper sausage neural network as an instance of GCL. Lastly, we propose a GCL solution to the “two-spirals” pattern recognition problem. The final experimental results show that the new method is feasible and efficient.

This paper develops a new dimensionality reduction method, named Biomimetic Uncorrelated Locality Discriminant Projection (BULDP), for face recognition. It is based on unsupervised discriminant projection and two human bionic characteristics: principle of homology continuity and principle of heterogeneous similarity. With these two human bionic characteristics, we propose a novel adjacency coefficient representation, which does not only capture the category information between different samples, but also reflects the continuity between similar samples and the similarity between different samples. By applying this new adjacency coefficient into the unsupervised discriminant projection, it can be shown that we can transform the original data space into an uncorrelated discriminant subspace. A detailed solution of the proposed BULDP is given based on singular value decomposition. Moreover, we also develop a nonlinear version of our BULDP using kernel functions for nonlinear dimensionality reduction. The performance of the proposed algorithms is evaluated and compared with the state-of-the-art methods on four public benchmarks for face recognition. Experimental results show that the proposed BULDP method and its nonlinear version achieve much competitive recognition performance.

This paper describes a special-purpose neural computing system for face identification. The system architecture and hardware implementation are introduced in detail. An algorithm based on biomimetic pattern recognition has been embedded. For the total 1200 tests for face identification, the false rejection rate is 3.7% and the false acceptance rate is 0.7%.