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Automatic identity recognition from ear images is an active research topic in the biometric community. The ability to secretly acquire images of the ear remotely and the stability of the ear shape over time make this technology a promising alternative for surveillance, authentication, and forensic applications. In recent years, significant research has been conducted in this area. Nevertheless, challenges remain that limit the commercial use of this technology. Several phases of the ear recognition system have been studied in the literature, from ear detection, normalization, and feature extraction to classification. This paper reviews the most recent methods used to describe and classify biometric features of the ear. We propose a first taxonomy to group existing approaches to ear recognition, including 2D, 3D, and combined 2D and 3D methods, as well as an overview of historical advances in this field. It is well known that data and algorithms are the essential components in biometrics, particularly in-ear recognition. However, early ear recognition datasets were very limited and collected in laboratory with controlled environments. With the wider use of deep neural networks, a considerable amount of training data has become necessary if acceptable ear recognition performance is to be achieved. As a consequence, current ear recognition datasets have increased significantly in size. This paper gives an overview of the chronological evolution of ear recognition datasets and compares the performance of conventional vs. deep learning methods on several datasets. We proposed a second taxonomy to classify the existing databases, including 2D, 3D, and video ear datasets. Finally, some open challenges and trends are debated for future research. [ABSTRACT FROM AUTHOR]

• An automatic region of interest segmentation method • A set of rules as a metric to measure the region of interest segmentation results • Analysis of thermal matrices as new data source for feature extraction • Design of experiments in a more robust manner taking into account most of the images in the database • Genetic algorithm for hyperparameter tunning for the Artificial Neural Network Breast thermography images are a new type of data that has been analyzed in recent years in order to detect abnormalities, which can lead to a future breast cancer. This paper proposes a methodology for breast thermal image classification, which is useful in Computer-Aided Detection Systems. The main contribution is an automatic method to segment the region of interest (ROI) based on local operations, local analysis, interpolation and statistical operators. For our experiments, we used an image database that is widely used in this research area, obtaining accuracy results between 90.17% and 98.33%, which are competitive with respect to related works. [ABSTRACT FROM AUTHOR]

Ten years ago, linear models were applied in various domains. Before application of the algorithms, several current studies extracted features presumed to represent parochial markings from the data using engineering techniques. Recently the deep learning domain offered opportunities to directly feed data into the model without any extensive hand-crafted feature engineering techniques. In this paper, the proposed framework does the feature extraction in a non-supervised (i.e. self-supervised) manner using both Contextual Long Short-Term Memory (CLSTM) and Contextual Convolutional Neural Networks (CCNN). We can then concatenate data obtained from the CLSTM and CCNN blocks, feed it into the Attention block, pass it through the Multilayer Perceptron (MLP) block, ultimately passing it through a terminal layer for classification. The task involved here was non-trivial as there is a major challenge in implementing our model to solve the time series classification (TSC) problem: overfitting. We deal with this challenge as follows; firstly, we adjusted the number of neurons in each of the stages. Secondly we introduced dropouts after every layer in each stage of this model. Finally experiments regarding the University of California Riverside (UCR) dataset indicates the model's superiority. [ABSTRACT FROM AUTHOR]

In real-world applications, factors such as head pose variation, occlusion, and poor image quality make facial expression recognition (FER) an open challenge. In this paper, a novel conditional convolutional neural network enhanced random forest (CoNERF) is proposed for FER in unconstrained environment. Our method extracts robust deep salient features from saliency-guided facial patches to reduce the influence from various distortion types, such as illumination, occlusion, low image resolution, etc. A conditional CoNERF is devised to enhance decision trees with the capability of representation learning from transferred convolutional neural networks and to model facial expression of different perspectives with conditional probabilistic learning. In the learning process, we introduce a neurally connected split function (NCSF) as the node splitting strategy in the CoNERF. Experiments were conducted using public CK+, JAFFE, multi-view BU-3DEF and LFW datasets. Compared to the state-of-the-art methods, the proposed method achieved much improved performance and great robustness with an average accuracy of 94.09% on the multi-view BU-3DEF dataset, 99.02% on CK+ and JAFFE frontal facial datasets, and 60.9% on LFW dataset. In addition, in contrast to deep neural networks which require large-scale training data, conditional CoNERF performs well even when there are only a small amount of training data. [ABSTRACT FROM AUTHOR]

This paper reviews the fingerprint classification literature looking at the problem from a double perspective. We first deal with feature extraction methods, including the different models considered for singular point detection and for orientation map extraction. Then, we focus on the different learning models considered to build the classifiers used to label new fingerprints. Taxonomies and classifications for the feature extraction, singular point detection, orientation extraction and learning methods are presented. A critical view of the existing literature have led us to present a discussion on the existing methods and their drawbacks such as difficulty in their reimplementation, lack of details or major differences in their evaluations procedures. On this account, an experimental analysis of the most relevant methods is carried out in the second part of this paper, and a new method based on their combination is presented. [ABSTRACT FROM AUTHOR]

Several methods for the location and classification of faults in power transmission lines using computational intelligence and digital signal processing techniques have been described in literature. Artificial neural networks (ANNs) and wavelet transform (WT) have drawn significant attention lately, but they present some drawbacks when dealing with power systems faults where data are often contaminated by noise. This paper proposes an approach by combining independent component analysis (ICA) with travelling wave (TW) theory and support vector machine (SVM). The approach is adequate to locate and recognize faults in high-voltage (HV) transmission lines, while the acquired signals are noisy. Experiments performed for distinct types and locations of faults in a real transmission line model have shown that the proposed combined methods are able to provide excellent performance in fault location. The obtained errors are lower than 1% and accuracy is 100% for the classification of fault signals with noise. It can be stated that this method presents better performance than those regarding the main conventional techniques such as wavelets and neural networks in the presence of noise. [ABSTRACT FROM AUTHOR]

Traffic signs are one of the most important safety elements in a road network. Particularly, road markings provide information about the limits and direction of each road lane, or warn the drivers about potential danger. The optimal condition of road markings contributes to a better road safety. Mobile Laser Scanning technology can be used for infrastructure inspection and specifically for traffic sign detection and inventory. This paper presents a methodology for the detection and semantic characterization of the most common road markings, namely pedestrian crossings and arrows. The 3D point cloud data acquired by a LYNX Mobile Mapper system is filtered in order to isolate reflective points in the road, and each single element is hierarchically classified using Neural Networks. State of the art results are obtained for the extraction and classification of the markings, with F-scores of 94% and 96% respectively. Finally, data from classified markings are exported to a GIS layer and maintenance criteria based on the aforementioned data are proposed. [ABSTRACT FROM AUTHOR]

Globally, the rate of preterm births are increasing, thus resulting in significant health, development and economic problems. Current methods for the early detection of such births are inadequate. Nevertheless, there has been some evidence that the analysis of uterine electrical signals, collected from the abdominal surface, could provide an independent and easier way to diagnose true labour and detect the onset of preterm delivery. Using advanced machine learning algorithms, in conjunction with Electrohysterography signal processing, numerous studies have focused on detecting true labour several days prior to the event. However, in this paper, the Electrohysterography signals have been used to detect preterm births. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Several new features from Electromyography studies have been utilised, as well as feature-ranking techniques to determine their discriminative capabilities in detecting term and preterm records. Seven different artificial neural networks were then used to identify these records. The results illustrate that the combination of the Levenberg–Marquardt trained Feed-Forward Neural Network, Radial Basis Function Neural Network and the Random Neural Network classifiers performed the best, with 91% for sensitivity, 84% for specificity, 94% for the area under the curve and 12% for the mean error rate. [ABSTRACT FROM AUTHOR]

For years, researchers in neural network (NN) area have been carried out much productive research in improving the generalization ability of NNs. In this paper, a novel neural network design algorithm is presented for solving multi-class problems, structure decomposition based on Skeletonization (SDBSkeletonization), which is to simplify NNs further. The proposed method decomposes a complex multi-class problem into a set of two-class problems, each of which can be regarded as an individual problem. After learning all these individual problems in parallel with Skeletonization algorithm, we then integrate these results to final decision. In addition, Skeletonization solves the classification problem based on automatic feature extraction. This perspective gives a broader range of application of our method. Our experimental results on Waveform and Handwritten Digits database demonstrate that SDBSkeletonization improves the overall classification performance. [ABSTRACT FROM AUTHOR]

• This systematic review aims to present the emerging techniques used to improve accuracy of thermography based diagnosis systems for breast cancer. • Details of feature extraction techniques by various authors have been discussed with improvements obtained. • Methods to overcome shortcomings of breast thermography as an adjunct tool have also been mentioned. One of the most prevalent cancers among women is the breast cancer. Accurate diagnosis of breast cancer at an early stage can reduce the mortality associated with this disease. Infrared Breast Thermography, which is a screening tool used to measure the temperature distribution of breast tissue, is a suitable adjunct tool to mammography. Breast thermography has many advantages as it is non-invasive, safe and painless. Thermographic image and usage of artificial neural networks have improved the accuracy of thermography in early diagnosis of breast abnormality. This paper presents survey based on the main steps of computer aided detection systems: image acquisition protocols, segmentation techniques, feature extraction and classification methods, used in the field of breast thermography over the past few decades. The detailed survey emphasizes on the improved reliability of breast thermography.This has become possible with the utilization of machine learning techniques for correct classification of breast thermograms. Numerical Simulation can be used as a supporting method to overcome high false positive rates in thermographic diagnosis. The paper also presents future recommendations to utilize recent machine learning advances in real time. [ABSTRACT FROM AUTHOR]

Dental caries, alternatively called tooth decay is considered as the most general and rapidly increasing diseases. According to the statistics, nearly 90% of the adults confront the issue of dental caries. Hence for proper dental care and health, early detection and diagnosis concerning the caries lesion plays a mandatory role. To achieve this, technique of Imaging Processing is being adopted which further helps the specialists to carry out precise diagnosis. Dental Caries can be categorized into different types depending on its position as well as seriousness. This categorization holds significant to carry out diagnosis and plan for the treatment of dental disease. The paper recommends the new HNN (Hybrid Neural Network) technique for detecting dental caries depending on, its type and therefore precisely classify the caries affected layer. HNN is a blend of ANN (Artificial Neural Network and DNN (Deep neural network) techniques. To obtain image classification, deep neural network relies upon the stacked sparse auto-encoder. Thereafter authentic information can be fetched from data via supervised fine-tuning and unsupervised pre-training. On the other hand ANN (Artificial neural network) relies upon the logistic regression for classifying the dental caries affected layer. Various processes involved in dental input image are as following: l. Pre-Processing, 2. Segmentation, 3. Feature Extraction and 4.Classification. Caries are categorized into 4 different layers by the HNN, namely: Enamel, Dentin, Pulp and root lesions. The proposed system yield in effective output against the input image provided and precisely categorizes the caries level. Hybrid neural network is the innovative technique which is giving good result in terms of accuracy and processing time. [ABSTRACT FROM AUTHOR]

• This paper imparts a road map for the researchers of biometric recognition system. • Highlights both Unimodal and multimodal biometric system. • Covering diagrammatic view and major characteristics of all the biometric traits. • Focus is given to the feature extraction methods, selection of the classifier. • Datasets used and the accuracy achieved to explore the main ideas of the authors. Biometrics is the branch of science that deals with the identification and verification of an individual based on the physiological and behavioral traits. These traits or identifiers are permanent, unique and can separate one individual from another. Biometric recognition systems integrate complex definitional, technological and operational selection under various contexts. The systems are not going to replace the authentication tools and technologies, but the combination of biometric approaches and authentication methods to help in improving the security aspects of the applications where user cooperation can be inferred. Biometric based recognition methods and tools have become popular for the development of many useful, challenging and widely accepted applications such as security issues, surveillance, forensic investigations, fraudulent technologies, identity access management and access control. These systems also help to identify an individual in group of industrial networks, home/office building and control system. For the successful implementation of the biometric systems, deep artificial neural networks are in great demand. These systems can be built up either on the single modality or multiple modalities. This article explicates the comprehensive and deep survey that compactly and systematically summarizes the literature work done on unimodal and multimodal biometric systems and analyzes the feature extraction techniques, classifiers, datasets, results, efficiency and reliability of the system with high and multi-dimensional perspectives. This article also justifies in detail the classical methods, influential methods and taxonomy based on the biometric attributes. The goal is to aware the researchers of this area regarding various dimensions for the development of biometric systems to enhance the security aspects. The article begins with the fundamentals, types, need of system, challenges, uncertainties, motivations and then to the survey work. The tabular representation prepares for each biometric trait shows the author, year, major findings and results achieved with the synthesis analysis and the evaluation. The article finally ends up with the 3D biometric, a future perspective and concluding remarks. [ABSTRACT FROM AUTHOR]

• CNNs are considered for dealing with the flood susceptibility mapping task. • Two different CNN frameworks of classification and feature extraction are presented. • Three data presentation forms are designed for the proposed CNN frameworks. • Reliable flood susceptibility maps can be obtained by using the proposed CNNs. • Prediction performance of SVM can be improved using the CNNs for feature extraction. Flood is a very destructive natural disaster in the world, which seriously threatens the safety of human life and property. In this paper, the most popular convolutional neural network (CNN) is introduced to assess flood susceptibility in Shangyou County, China. The main contributions of this study are summarized as follows. First, the CNN technique is used for flood susceptibility mapping through two different CNN classification and feature extraction frameworks. Second, three data presentation methods are designed in the CNN architecture to fit the two proposed frameworks. To construct the proposed CNN-based methods, 13 flood triggering factors related to historical flood events in the study area were prepared. The performance of these CNN-based methods was evaluated using several objective criteria in comparison to the conventional support vector machine (SVM) classifier. Experiments results demonstrate that all the CNN-based methods can produce more reliable and practical flood susceptibility maps. For example, the proposed CNN-based classifiers were 0.022–0.054 higher than SVM in terms of area under the curve (AUC). In addition, in the classification process, CNN-based feature extraction can effectively improve the prediction capability of SVM by 0.021–0.051 in terms of AUC. Therefore, the proposed CNN frameworks can help mitigate and manage floods. [ABSTRACT FROM AUTHOR]