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A Wavelet-Based Memory autoencoder for Noncontact Fingerprint Presentation Attack Detection

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 8717-8730页

作者： Liu, Yi-Peng Yu, Hangtao Fang, Haonan Li, Zhanqing Chen, Peng Liang, Ronghua Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Fingerprint presentation attack detection (FPAD) is essential in fingerprint identification systems. Noncontact methods such as fingerprint biometrics are becoming popular because they are not affected by skin conditions and there are no hygiene issues. However, most of the existing noncontact FPAD methods are supervised methods with poor generalizability and poor performance during events such as unseen presentation attacks (PAs). Moreover, easily overlooked frequency domain information contributes to the fingerprint antispoofing task. Therefore, we propose a wavelet-based memory-augmented autoencoder that fully utilizes the frequency domain information. Specifically, the model first decomposes the input image into high- and low-frequency information and extracts features separately. Subsequently, we propose a frequency complementary connection (FCC) module to realize the fusion and complementation of frequency domain information at the feature level. Moreover, a memory distance expansion loss is proposed to keep the memory module diverse. Experiments are conducted to verify the effectiveness of the method. The code of our model is available on https://***/SuperIOyht/WaveMemAE.

关键词： Fingerprint recognition Feature extraction Frequency-domain analysis Image reconstruction Memory modules Image matching Biological system modeling Presentation attack detection biometrics noncontact fingerprint anomaly detection autoencoder wavelet frequency memory

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autoencoder Image Denoising to Increase Optical Character Recognition Performance in Text Conversion

Autoencoder Image Denoising to Increase Optical Character Re...

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International Conference on Advanced Creative Networks and Intelligent Systems (ICACNIS) - Blockchain Technology, Intelligent Systems, and the Applications for Human Life

作者： Alamsyah, Nur Fauzan, Mohamad Nurkamal Putrada, Aji Gautama Pane, Syafrial Fachri Telkom Univ Adv & Creat Networks Res Ctr Bandung Indonesia

ISBN: (纸本)9798350334449

Document digitization has an important role in helping the company's activities be more efficient, such as detecting text in invoice document images using optical character recognition (OCR). However, writing in images has many problems, especially tediously saved documents that can cause noise or interference in the picture, resulting in difficultly recognized writing. Our research aims to build an autoencoder for denoising text images and evaluate the OCR's performance in converting the denoised image into text. The first step in the research is to test the OCR characteristics on the original text image and the text image given Gaussian noise. The next step is to build the optimal autoencoder model for denoising by studying the effect of dataset size and optimizer type. The last step is to test the OCR performance on the denoised text image produced by the optimum autoencoder model. The test results show that datasetsize affects denoising performance and OCR performance. From several autoencoder models compared, the autoencoder with dataset size = 40 has the optimum performance, where the MSE values of the model for train and validation are 1277 and 1385, respectively. With images denoised from the optimum model, the OCR performance in converting images into text is 100%.

关键词： optical character recognition Gaussian noise denoising autoencoder text image

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ContrastNet: Unsupervised feature learning by autoencoder and prototypical contrastive learning for hyperspectral imagery classification

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NEUROCOMPUTING 2021年 460卷 71-83页

作者： Cao, Zeyu Li, Xiaorun Feng, Yueming Chen, Shuhan Xia, Chaoqun Zhao, Liaoying Zhejiang Univ Coll Elect Engn 38 Zheda Rd Hangzhou 310027 Peoples R China State Grid Jiaxing Power Supply Co Jiaxing 314100 Zhejiang Peoples R China HangZhou Dianzi Univ China Inst Comp Applicat Technol Hangzhou 310018 Peoples R China

Hyperspectral classification is a fundamental problem for applying hyperspectral technology, and unsupervised learning is a promising direction to address the issue. Unsupervised feature learning algorithm extracts representative features, which help efficiently classify the hyperspectral pixels. This paper combines the popular contrastive learning method (prototypical contrastive learning) and the classic representation learning method (autoencoder) to design an unsupervised feature learning network for hyperspectral classification. First, two different encoders are used to extract different features as augmentation function in a contrastive network (ContrastNet). Second, the prototypical contrastive learning method is adopted to train a contrastive network. Third, features extracted by the contrastive network are used for classification. Experiments have proved that our two proposed autoencoder networks show good feature learning capabilities by themselves and the designed contrastive learning network can further learn better representative features from the two modules. The three modules compose an efficient framework for unsupervised feature learning. Our method also performs reasonably fast in the testing phase, which implies that it is applicable in practice under specific conditions. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Unsupervised learning autoencoder Contrastive learning Hyperspectral imagery classification

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An autoencoder with adaptive transfer learning for intelligent fault diagnosis of rotating machinery

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MEASUREMENT SCIENCE AND TECHNOLOGY 2021年第5期32卷

作者： Tang, Zhi Bo, Lin Liu, Xiaofeng Wei, Daiping Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

Under variable working conditions, a problem arises, which is that it is difficult to obtain enough labeled data;to address this problem, an adaptive transfer autoencoder (ATAE) is established to diagnose faults in rotating machinery. First, a data adaptation module, which calculates the maximum mean discrepancy for the network hidden-layer data in reproducing kernel Hilbert space, is introduced to the autoencoder network, thus making the classification model operate under variable working conditions. Variation particle-swarm optimization is then invoked to optimize the data adaptation parameters. Finally, the k-nearest neighbors algorithm, as the classification layer of the network, identifies the state of health of the rotating machinery. The capabilities of the intelligent fault-diagnosis network are verified using vibration signals from a bearing test rig and a gearbox test rig. The experimental results suggest that, compared with state-of-the-art diagnosis methods, the proposed ATAE network can significantly boost diagnostic performance in the absence of target vibration signal labels.

关键词： rotating machinery intelligent fault diagnosis autoencoder transfer learning adaptive optimization

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Evolving Domain Generalization via Latent Structure-Aware Sequential autoencoder

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第12期45卷 14514-14527页

作者： Qin, Tiexin Wang, Shiqi Li, Haoliang City Univ Hong Kong Dept Elect & Engn Kowloon Hong Kong Peoples R China City Univ Hong Kong Sch Comp Sci Kowloon Hong Kong Peoples R China

Domain generalization (DG) refers to the problem of generalizing machine learning systems to out-of-distribution (OOD) data with knowledge learned from several provided source domains. Most prior works confine themselves to stationary and discrete environments to tackle such generalization issue arising fromOODdata. However, in practice, manytasks in non-stationary environments (e.g., autonomous-driving car system, sensor measurement) involve more complex and continuously evolving domain drift, emerging new challenges for model deployment. In this paper, we first formulate this setting as the problem of evolving domain generalization. To deal with the continuously changing domains, we propose MMD-LSAE, a novel framework that learns to capture the evolving patterns among domains for better generalization. Specifically, MMD-LSAE characterizes OOD data in non-stationary environments with two types of distribution shifts: covariate shift and concept shift, and employs deep autoencoder modules to infer their dynamics in latent space separately. In these modules, the inferred posterior distributions of latent codes are optimized to align with their corresponding prior distributions via minimizing maximum mean discrepancy (MMD). We theoretically verify that MMD-LSAE has the inherent capability to implicitly facilitate mutual information maximization, which can promote superior representation learning and improved generalization of themodel. Furthermore, the experimental results on both synthetic and real-world datasets show that our proposed approach can consistently achieve favorable performance based on the evolving domain generalization setting.

关键词： Domain generalization non-stationary environments autoencoder kernel methods

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High Accuracy Individual Identification Model of Crested Ibis <italic>(Nipponia Nippon)</italic>Based on autoencoder With Self-Attention

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IEEE ACCESS 2020年 8卷 41062-41070页

作者： Xie, Jiangjian Yang, Jun Ding, Changqing Li, Wenbin Beijing Forestry Univ Sch Technol Beijing 100083 Peoples R China Beijing Forestry Univ Key Lab Natl Forestry & Grass Land Adm Forestry E Beijing 100083 Peoples R China Beijing Forestry Univ Sch Ecol & Nat Conservat Beijing 100083 Peoples R China

As the population and the distribution of Crested Ibis (Nipponia nippon) become larger, it is necessary to propose a highly efficient census method to estimate the population size of the Crested Ibis. Passive acoustic monitoring (PAM) has a very good prospect for the Crested Ibis monitoring. To realize the automatic census of the Crested Ibis with PAM, the automatic individual identification method based on the vocalization is the key technology. A novel individual identification model was proposed in this paper, which built the autoencoder based on LSTM to obtain the meaningful latent representation from the raw recording directly, further, embedded self-attention and putted forward a combined training mode to achieve distinctive latent representation. With this model, nine Crested Ibis individuals were identified accurately, the highest accuracy is 0.971, and the average accuracy reaches 0.958. As for other three species, Little owl (Athene noctua), Chiffchaff (Phylloscopus collybita) and Tree pipit (Anthus trivialis), the better performances were achieved than the existing method, which means the proposed model can provide an alternative method for the individual identification of other bird species.

关键词： Nipponia nippon individual identification autoencoder self-attention LSTM

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Novel method for the detection of adulterants in coffee and the determination of a coffee's geographical origin using near infrared spectroscopy complemented by an autoencoder

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INTERNATIONAL JOURNAL OF FOOD SCIENCE AND TECHNOLOGY 2023年第3期58卷 1284-1298页

作者： Munyendo, Leah Njoroge, Daniel Zhang, Yanyan Hitzmann, Bernd Univ Hohenheim Dept Proc Analyt & Cereal Sci Garbenstr 23 D-70599 Stuttgart Germany Dedan Kimathi Univ Technol Inst Food Bioresources Technol Dedan Kimathi 10143 Nyeri Kenya Univ Hohenheim Dept Flavor Chem Fruwirthstr 12 D-70599 Stuttgart Germany

Coffee authenticity is a foundational aspect of quality when considering coffee's market value. This has become important given frequent adulteration and mislabelling for economic gains. Therefore, this research aimed to investigate the ability of a deep autoencoder neural network to detect adulterants in roasted coffee and to determine a coffee's geographical origin (roasted) using near infrared (NIR) spectroscopy. Arabica coffee was adulterated with robusta coffee or chicory at adulteration levels ranging from 2.5% to 30% in increments of 2.5% at light, medium and dark roast levels. First, the autoencoder was trained using pure arabica coffee before being used to detect the presence of adulterants in the samples. Furthermore, it was used to determine the geographical origin of coffee. All samples adulterated with chicory were detectable by the autoencoder at all roast levels. In the case of robusta-adulterated samples, detection was possible at adulteration levels above 7.5% at medium and dark roasts. Additionally, it was possible to differentiate coffee samples from different geographical origins. PCA analysis of adulterated samples showed grouping based on the type and concentration of the adulterant. In conclusion, using an autoencoder neural network in conjunction with NIR spectroscopy could be a reliable technique to ensure coffee authenticity.

关键词： adulteration autoencoder chicory coffee geographical origin NIR spectroscopy

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Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 247卷 108114-108114页

作者： Dai, Peishan Shi, Yun Lu, Da Zhou, Ying Luo, Jialin He, Zhuang Chen, Zailiang Zou, Beiji Tang, Hui Huang, Zhongchao Liao, Shenghui Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China Cent South Univ Xiangya Hosp 2 Dept Psychiat Changsha 410083 Hunan Peoples R China Cent South Univ Xiangya Hosp 2 Natl Clin Res Ctr Mental Disorders Changsha 410083 Hunan Peoples R China Cent South Univ Sch Basic Med Sci Dept Biomed Engn Changsha 410083 Hunan Peoples R China

Background and objective: Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. Methods: We developed a Residual Denoising autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity (FC) was extracted from fMRI data as features. The framework addresses site heterogeneity by employing the Combat method to harmonize feature distribution differences. A feature selection method based on Fisher scores was used to reduce redundant information in the features. A data augmentation strategy using a Synthetic Minority Over -sampling Technique algorithm based on Extended Frobenius Norm measure was incorporated to increase the sample size. Furthermore, a residual module was integrated into the autoencoder network to preserve important features and improve the classification accuracy. Results: We tested our framework on a large-scale, multisite fMRI dataset, which includes 189 rMDD patients and 427 healthy controls. The Res-DAE achieved an average accuracy of 75.1 % (sensitivity = 69 %, specificity = 77.8 %) in cross-validation, thereby outperforming comparison methods. In a larger dataset that also includes first-episode depression (comprising 832 MDD patients and 779 healthy controls), the accuracy reached 70 %. Conclusions: We proposed a deep learning framework that can effectively classify rMDD and 33 identify the altered FC associated with rMDD. Our study may reveal changes in brain function 34 associated with rMDD and provide assistance for the diagnosis and treatment of rMDD.

关键词： Recurrent major depressive disorder Functional magnetic resonance imaging Functional connectivity autoencoder Residual network Deep learning

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Disentangled-Multimodal Adversarial autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第12期39卷 4137-4149页

作者： Hu, Dan Zhang, Han Wu, Zhengwang Wang, Fan Wang, Li Smith, J. Keith Lin, Weili Li, Gang Shen, Dinggang Univ N Carolina Dept Radiol Chapel Hill NC 27599 USA Korea Univ Dept Artificial Intelligence Seoul 02841 South Korea

Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy of infant age prediction thanks to the complementary information provided by different imaging modalities. However, functional connectivity measured by fMRI during infancy is largely immature and noisy compared to the morphological features from sMRI, thus making the sMRI and fMRI fusion for infant brain analysis extremely challenging. With the conventional multimodal fusion strategies, adding fMRI data for age prediction has a high risk of introducing more noises than useful features, which would lead to reduced accuracy than that merely using sMRI data. To address this issue, we develop a novel model termed as disentangled-multimodal adversarial autoencoder (DMM-AAE) for infant age prediction based on multimodal brain MRI. Specifically, we disentangle the latent variables of autoencoder into common and specific codes to represent the shared and complementary information among modalities, respectively. Then, cross-reconstruction requirement and common-specific distance ratio loss are designed as regularizations to ensure the effectiveness and thoroughness of the disentanglement. By arranging relatively independent autoencoders to separate the modalities and employing disentanglement under cross-reconstruction requirement to integrate them, our DMM-AAE method effectively restrains the possible interference cross modalities, while realizing effective information fusion. Taking advantage of the latent variable disentanglement, a new strategy is further proposed and embedded into DMM-AAE to address the issue of incompleteness of the multimodal neuroimages, which can also be used as an independent algorithm for missing modality imputation. By taking six types of cortical morphometric features from sMRI and brain functional connectivity from fMRI as predictors, the superiority of the proposed DMM-AAE is validated o

关键词： Functional magnetic resonance imaging Brain modeling Predictive models Data models Biological system modeling Biomarkers Infant age prediction autoencoder multimodal machine learning magnetic resonance imaging

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Signal-to-noise ratio enhancement for Raman spectra based on optimized Raman spectrometer and convolutional denoising autoencoder

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JOURNAL OF RAMAN SPECTROSCOPY 2021年第4期52卷 890-900页

作者： Fan, Xian-guang Zeng, Yingjie Zhi, Yu-Liang Nie, Ting Xu, Ying-jie Wang, Xin Xiamen Univ Dept Instrumental & Elect Engn Xiamen 361102 Fujian Peoples R China Xiamen Key Lab Optoelect Transducer Technol Fujian Key Lab Univ & Coll Transducer Technol Xiamen Peoples R China

The signal-noise ratio plays a key role in acquiring plentiful chemical structural information in the Raman spectrometer. The miniature spectrometer is generally compact at the expense of performance. In this work, we proposed a compact, signal-to-noise ratio (SNR) enhancement of the Raman spectrometer by the optimization of optical structure and a noise reduction method. Concerning its optical structure, the Raman spectrometer is increasing the intensity by adding an off-the-shelf cylindrical lens. On the other side of the algorithm, a relevant automatic denoising method of convolutional denoising autoencoder (CDAE) is proposed to further advance the SNR in Raman spectra without manual intervention. The results indicate the performance of the compact Raman spectrometer could increase to a certain extent by testing with 785 nm laser and Ne/Ar source. Besides, by using CDAE to deal with contaminated Raman spectra, a higher SNR is obtained. The results demonstrate that the improvement of the hardware and algorithm is effective for removing the noisy Raman signal and achieving higher SNR. This result may be helpful in further improving the performance of integrated Raman spectrometers and research on miniaturized instruments.

关键词： autoencoder convolutional neural network Czerny-Turner denoising Raman spectrometer

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