A New Filter Generation Method in PCANet for Finger Vein Recognition

The manual feature extraction of the traditional method for vehicle license plates has no good robustness to change in diversity. And the high feature dimension that is extracted with Principal Component Analysis Network (PCANet) leads to low classification efficiency. For solving these problems, a method of vehicle license plate recognition based on PCANet and compressive sensing is proposed. First, PCANet is used to extract the feature from the images of characters. And then, the sparse measurement matrix which is a very sparse matrix and consistent with Restricted Isometry Property (RIP) condition of the compressed sensing is used to reduce the dimensions of extracted features. Finally, the Support Vector Machine (SVM) is used to train and recognize the features whose dimension has been reduced. Experimental results demonstrate that the proposed method has better performance than Convolutional Neural Network (CNN) in the recognition and time. Compared with no compression sensing, the proposed method has lower feature dimension for the increase of efficiency.

Medical image classification plays an important role in disease diagnosis since it can provide important reference information for doctors. The supervised convolutional neural networks (CNNs) such as DenseNet provide the versatile and effective method for medical image classification tasks, but they require large amounts of data with labels and involve complex and time-consuming training process. The unsupervised CNNs such as principal component analysis network (PCANet) need no labels for training but cannot provide desirable classification accuracy. To realize the accurate medical image classification in the case of a small training dataset, we have proposed a light-weighted hybrid neural network which consists of a modified PCANet cascaded with a simplified DenseNet. The modified PCANet has two stages, in which the network produces the effective feature maps at each stage by convoluting inputs with various learned kernels. The following simplified DenseNet with a small number of weights will take all feature maps produced by the PCANet as inputs and employ the dense shortcut connections to realize accurate medical image classification. To appreciate the performance of the proposed method, some experiments have been done on mammography and osteosarcoma histology images. Experimental results show that the proposed hybrid neural network is easy to train and it outperforms such popular CNN models as PCANet, ResNet and DenseNet in terms of classification accuracy, sensitivity and specificity.

Automatic targets recognition (ATR) for synthetic aperture radar (SAR) image is very important. ATR can be used in traffic management, national frontier safety, and so on. Traditional algorithms for SAR ATR is composed of extraction features and training classifier. The features are essential for the classification accuracy. However, choosing good features by hand is a hard task. The deep convolutional neural networks (CNNs) which can learn features automatically have got a great performance in natural images. However, the CNNs have many parameters and need a lot of data to train such networks. The remote-sensing data of SAR is limited. Then, the authors need a simple network which needs not much data and easy to train. The principal component analysis network (PCANet) is a shallow network that performs well in the recognition task and needs no hand features choosing. Though this network has produced a wide application in the natural images, it is rarely used in the SAR images. The experimental result of the moving and stationary target acquisition and recognition (MSTAR) dataset shows that the PCANet can achieve over 99% accuracy on ten-class targets. This result is better than traditional algorithms and is very close to the results of deep-learning methods.

Recent advancements in finger vein recognition technology: Methodology, challenges and opportunities

Robust two-dimensional PCANet with F-norm distance metric

Exploring finger vein based personal authentication for secure IoT

The current work on emotion recognition considering EEG signals has attracted significant attention due to its advantages in fields like mental health, neural computing, and human-computer interaction. Nonetheless, accurately classifying emotions using EEG signals poses a challenge, mainly due to issues such as noise, artefacts, and high dimensionality found within EEG data. Moreover, the current problem lies in extracting meaningful emotional features while efficiently addressing the above-described challenges to improve classification accuracy. Hence, this work proposed an approach for solving the challenges in emotion classification by presenting a model that integrates Principal Component Analysis (PCA) and Convolutional Neural Network (CNN). The PCA was used for removing noise and for dimensionality reduction. In contrast, the CNN was used for extracting emotion-related features and classifying emotions, mainly the two classes, arousal and valence. The proposed PCA-CNN model was evaluated considering the DEAP dataset, where the PCA-CNN model achieved 93.58% accuracy for arousal and 92.75% for valence for subject-dependent and 81.40% accuracy for arousal and 79.84% for valence for subject-independent. The findings also show that the PCA-CNN model provided better results in handling noise and reducing data complexity, ensuring high classification performance. The novelty of the PCA-CNN model is that PCA was used for preprocessing the EEG data, and CNN was used for feature optimization, which created a more efficient emotion classifier.

As an emerging biometric modality, finger vein recognition has received considerable attentions. However, recent studies have shown that finger vein biometrics is vulnerable to presentation attacks, i.e. printed versions of authorized individuals’ finger veins could be used to gain access to facilities or services. In this paper, we have designed a specific shallow convolutional neural network (CNN) for finger vein presentation attack detection (PAD), which is called as FPNet for short. The proposed FPNet has been evaluated on a public-database and an intra-database. Lots of h × h patches have been extracted from vein images with a stride s for dataset augmentation and then used to train our networks without any pre-trained model. For further improving models’ generalizability and robustness, training patches of two databases have been mixed together and our best model has achieved an accuracy of 100% on both test datasets, clearly outperforming state-of-the-art methods.

Human activity recognition is an important and challenging topic for computer vision research community. Action representation using deep learning models are currently the dominant technique compared with other methods. However, supervised convolutional neural networks require large computational and memory resources to optimize their parameters. Recently, a simple unsupervised deep learning architecture Principal Component Analysis Network (PCANet) has emerged as an alternative of Convolutional Neural Networks (CNNs) and has significant accomplishments in various vision applications. Meanwhile, encoding and representation techniques using Bag of Words (BoW) and Vector of Locally Aggregated Descriptors (VLAD) have demonstrated great success for several visual tasks specifically in activity recognition. This work presents a novel human activity recognition technique by combining global and local features of PCANet with BoW and VLAD encoding schemes. Both global and local features are learned by PCANet utilizing selected frames from each action video. After that the dimensionality of these features is decreased via Whitening PCA (WPCA). Then encoding schemes are applied on both features to represent the final descriptors for each action. Ultimately, Support Vector Machines classifier (SVM) is trained for recognition process. Several experiments are conducted on UCF sports dataset to evaluate our method. All experimental results utilizing leave-one-out-cross validation (LOOCV) strategy are satisfactorv and comparable.

In this work, we propose an efficient joint Bayesian model that expanded with soft biometric traits for finger vein recognition, which is different from the existing mainstream finger vein recognition methods that extract features from the region of interest (ROI) of finger vein images. First, a two-branch convolutional neural networks (CNNs) is designed to extract feature simultaneously from finger shape images and ROI vein images, respectively, the aim of which is to extract more information from different parts of fingers. Then, two different features are fused by concatenating. Finally, a joint Bayesian recognition loss is used to train the proposed two-branch CNN which is to promote intra-class variance minimization and inter-class variation maximization. Further, in our framework, a end-to-end learning method of CNNs and joint Bayesian recognition loss is achieved to optimize the parameters. Experiments performed on two public finger vein databases demonstrate that the proposed model is efficient and achieves better performance than state-of-the-art methods.

Finger vein recognition is a promising biometric authentication technique that depends on the unique features of vein patterns in the finger for recognition. The existing finger vein recognition methods are based on minutiae features or binary features such as LBP, LLBP, PBBM etc. or from the entire vein pattern. However, the minutiae-based features cannot accurately represent the structural or anatomical aspects of the vein pattern. These issues with the minutia feature led to increased false matches. Recognition based on binary features have limitations such as increased false matches, sensitivity to the translation and rotation, security and privacy issues etc. A feature representation based on the anatomy of vein patterns can be an alternative solution to improve the recognition performance. In the IJCB 2020 conference, we showed that every finger vein image contains one or more of a kind of 4 special vein patterns which we refereed as Fork, Eye, Bridge, and Arch (FEBA). In this paper, we further enlarge this set to 6 vein patterns (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> EB <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> A) by identifying two variations in the Fork and Bridge vein patterns. Based on 6 anatomical features of the possible 6 vein patterns in a vein image, we define a 6 × 6 feature matrix representation for finger vein images. Since this feature representation is based on the anatomical properties of the local vein patterns, it provides template security. Further we show that, the proposed feature representation is invariant to scaling, translation, and rotation changes. The experimental results using two open datasets and an in-house dataset show that the proposed method has a better recognition performance when compared to the existing approaches with an EER around 0.02% and an average recognition accuracy of 98%.

The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, ranging from the index, middle, and ring finger are combined to form a multi-instance finger vein representation. This fusion approach is preferred since it can be performed without requiring additional sensors or feature extractors. To combine different instances of finger vein effectively, score level fusion is adopted to allow greater compatibility among the wide range of matches. Towards this end, two methods are proposed: Bayesian optimized support vector machine (SVM) score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF). The fusion results are incrementally improved by optimizing the hyperparameters of the HOG feature, SVM matcher, and the weighted sum of score level fusion using the Bayesian optimization approach. This is considered a kind of knowledge-based approach that takes into account the previous optimization attempts or trials to determine the next optimization trial, making it an efficient optimizer. By using stratified cross-validation in the training process, the proposed method is able to achieve the lowest EER of 0.48% and 0.22% for the SDUMLA-HMT dataset and UTFVP dataset, respectively.

Finger vein recognition is a biometric technology using finger veins to authenticate a person, and due to its high degree of uniqueness, liveness, and safety, it is widely used. The traditional Symmetric Local Graph Structure (SLGS) method only considers the relationship between the image pixels as a dominating set, and uses the relevant theories to tap image features. In order to better extract finger vein features, taking into account location information and direction information between the pixels of the image, this paper presents a novel finger vein feature extraction method, Multi-Orientation Weighted Symmetric Local Graph Structure (MOW-SLGS), which assigns weight to each edge according to the positional relationship between the edge and the target pixel. In addition, we use the Extreme Learning Machine (ELM) classifier to train and classify the vein feature extracted by the MOW-SLGS method. Experiments show that the proposed method has better performance than traditional methods.

Recently, finger vein sensors have been embedded in all kinds of electronic devices for personal identification, such as intelligent door locks and attendance machines. The embedded sensors are generally small, thus capturing only part of the finger vein. However, prior studies have focused on near-full finger vein recognition, without considering the partial finger vein image caused by the small imaging window of the finger vein sensor. This paper aims to study personal identification based on partial finger vein images, known as small-area finger vein recognition. The effect of the small-area finger vein on recognition performance is first analyzed by cutting out the local part from the near-full finger vein image to model a small-area finger vein image. Second, a small-area finger vein database is built using a commercial finger vein imaging device, in which the vein pattern from approximately one-third of one adult finger is captured. To explore more discriminative information from small-area finger vein images, we propose a locality-constrained consistent dictionary learning (LCDL) method to fuse multiple features for small-area finger vein recognition. Finally, the proposed method is evaluated on the self-built small-area finger vein database and four synthetic small-area finger vein databases. Experimental results show the promising recognition performance of the proposed method.

Finger vein biometric has become most promising recognition method due to its accuracy, reliability and security. This paper discusses a novel technique for finger veins features extraction using Discrete Wavelet Packet Transform (DWPT) based method. The DWPT without HH subband decomposition is applied on ROI of 96×64 size finger veins image up to third level. The average standard deviation and average energy of each decomposition level are used for the creation of features vector database. The Euclidean, City Block and Canberra distance classifiers are used for the classification of finger veins images. The performance of proposed method is evaluated on the standard finger veins image ROI database of SDUMLA Shandong University. Experimental results show that the proposed method gives better results as compare to the standard Discrete Wavelet Transform (DWT) and DWPT Methods.