Facial hyperspectral image analysis has become a popular topic since it provides additional spectral information on subjects unlike the 2D face imagery which only has spatial information, hence it has an opportunity to improve face recognition accuracy. Three new methods for feature extraction for facial hyperspectral image classification are proposed. The methods employ a three-dimensional discrete wavelet transform (3D-DWT) to extract features from facial hyperspectral images. One of the advantages of 3D-DWT for feature extraction in hyperspectral images is that the horizontal, vertical and spectral information are processed in parallel. The most important characteristic of 3D-DWT is decomposing hyperspectral images into a set of spatio-spectral frequency subbands. The study proposes three methods using 3D-DWT for feature extraction: 3D-subband energy, 3D-subband overlapping cube and 3D-global energy. The k-NN and collaborative representation-based classifier (CRC) are used to process extracted feature vector datasets, where classification accuracies are evaluated by four test scenarios. The results under different test scenarios revealed that accuracy of proposed 3D-DWT methods is superior to alternative methods using spatio-spectral classification.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2017.0082

3D discrete wavelet transform-based feature extraction for hyperspectral face recognition

Hyperspectral face recognition provides improved classification rates due to its abundant information in the face cubes of every subject in hyperspectral face databases. However, it is less popular in face recognition due to its difficulty in data acquisition, low signal-to-noise ratio, and high dimensionality. The authors compare five existing descriptors that are frequently used in 2D face recognition, and use collaborative representation classifier (CRC) with two voting techniques for hyperspectral face recognition. Experimental results demonstrate that, for PolyU-HSFD database, Gabor filter bank-based features are very robust to both Gaussian white noise and shot noise, and it achieves very competitive classification results. For CMU-HSFD database, when the noise level is low, histogram of oriented gradients (HOG) yields good classification results. In addition, when the noise level is high, raw facial images without feature extraction perform very well in term of correct classification rate. The local binary pattern and HOG descriptor are very sensitive to noise even though they achieve rather good classification rates if the facial images contain no noise. The best recognition result for the PolyU-HSFD is 96.4% ± 2.3 and for the CMU-HSFD is 98.0% ± 0.7.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2016.0722

Hyperspectral face recognition via feature extraction and CRC-based classifier

In this research, biometric authentication, which has been widely used for different purposes in the last quarter-century, is studied. Dorsal hand veins are used for biometric authentication. “Deep learning” (DL) and “generative adversarial networks” (GANs) are used together as keys in the study. A DL-GAN is obtained by combining deep learning and GAN. The developed DL-GAN method is tested on two separate databases. The adversarial network (DL-GAN) method is developed to increase the authentication process’s proportional value. For identity verification, dorsal hand veins with biometric physical properties are used. A multistep approach is used for selecting hand dorsal features, including preimage processing and effectively identifying individuals. The deep learning productive antinetwork method is used to effectively identify individuals based on the information obtained from the dorsal hand vein images. For the test in the study, two open access databases are used. These databases are the Jilin University - dorsal hand vein database and the 11K hands database. The results of the experiments performed on the dataset related to the dorsal hand vessels show that the DL-GAN method reaches an identity accuracy level of 98.36% and has an error rate of 2.47% and a standard accuracy of 0.19%. The accuracy of the experimental results in the second dataset is 96.43%, the equal error rate is 3.55% and the standard accuracy is 0.21%. The improved DL-GAN method obtains better results than physical biometric methods such as LBP, LPQ, GABOR, FGM, BGM and SIFT.

/pdf/human-identity-verification-from-biometric-dorsal-hand-vein-qbmav6p27m.pdf

Human Identity Verification From Biometric Dorsal Hand Vein Images Using the DL-GAN Method

Unsupervised Learning Model of Sparse Filtering Enhanced Using Wasserstein Distance for Intelligent Fault Diagnosis

Hyperspectral face recognition is a small sample size problem, where usually less than four hyperspectral cubes are available as training data. At the same time, hyperspectral face image acquires grayscale images over a series of continuous spectra which usually contain large redundant information or noise, especially in the near infrared spectrum bands. Therefore, dimensionality reduction and feature extraction are important tasks on this problem. This paper proposes a hierarchical clustering-based spectrum band selection method, which mitigates the influence of noise and extracts features from each spectra band by using the Gabor filter and the histograms of oriented gradients algorithm, In addition, the fusion of Hog and Gabor features was embedded into the nearest neighborhood-based classifier for performance comparison. The experimental results show that the proposed algorithm is time effective and provides robust performance.

/pdf/hierarchical-clustering-based-band-selection-algorithm-for-3i5arvh5f6.pdf

Hierarchical Clustering Based Band Selection Algorithm for Hyperspectral Face Recognition

: Accurate tool condition monitoring (TCM) is an important part for ensuring milling quality. However, due to the cost of TCM experiment, there are few labelled and a lot of unlabelled samples in the training set that significantly affect the accuracy of many machine learning models. A novel method based on comparative learning (CL) and Gramian angular field (GAF) is proposed for improving the performance of TCM. The cutting force signals of each channel of all samples (including labelled and unlabelled) collected in

A novel tool condition monitoring based on Gramian angular field and comparative learning

A soft sensor model based on CNN-BiLSTM and IHHO algorithm for Tennessee Eastman process

Hyperspectral imagery analysis has become a popular topic for improving face recognition accuracy. Nevertheless, it encounters difficulty in data acquisition, low signal-to-noise ratio, and high dimensionality. As a result, there exists a need to develop better algorithms in order to achieve higher classification rates. In this study, the authors propose a new method for hyperspectral face recognition with very competitive experimental results. Since there is a significant amount of noise in every spectral band, they reduce noise adaptively from each spectral band by using any image denoising method, e.g. block matching and 3D filtering. They then crop each face according to its eye coordinates so that translation invariance can be achieved. They conduct log-polar transform to each cropped face image and extract 2D Fourier spectrum from them. In this way, the extracted features are approximately invariant to translation, rotation, and scaling. They use the collaborative representation-based classifier with voting for hyperspectral face recognition. They perform some experiments to test the authors’ new method for hyperspectral face recognition with very promising results.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2015.0103

Hyperspectral face recognition with log-polar Fourier features and collaborative representation based voting classifiers

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Hyperspectral face recognition via feature extraction and CRC-based classifier

A novel tool condition monitoring based on Gramian angular field and comparative learning

A soft sensor model based on CNN-BiLSTM and IHHO algorithm for Tennessee Eastman process

Hyperspectral face recognition with log-polar Fourier features and collaborative representation based voting classifiers