Multimodal biometric recognition using human ear and palmprint

This paper proposes a method for race recognition from face images using local descriptors. The proposed method uses two types of local descriptors: local binary pattern (LBP) and Weber local descriptors (WLD). First, LBP and WLD histograms are obtained separately from blocks of normalized face image. Kruskal-Wallis feature selection technique is applied to the histograms to select the significant bins for race recognition. Then the selected bins from the two histograms are concatenated block by block to produce the final feature set of the face image. Minimum city block distance is used as a classifier. The experiments are conducted using gray scale FERET images with five race groups. Experimental results show that the proposed method has superior race recognition accuracies for all the five race groups compared to LBP and WLD alone.

Robust and discriminative feature extraction without any controlled light intensity condition is vital for a real-time face recognition system. The Weber Local Descriptor (WLD) is an effective and robust face representation algorithm. However, WLD actually exploits the contrast information, which can still be sensitive to illumination changes. To overcome this problem, in this article, we take gradients into account and propose a novel operator, called Weber Local Gradient Descriptor (WLGD).This method produces the fusion characteristic and describes the facial texture through the computation of horizontal and diagonal gradients respectively. Experimental results on the ORL face database and infrared face database demonstrate that the proposed WLGD algorithm outperforms some state-of-art methods.

Recognition of emotions based on facial expressions has received increasing attention from the scientific community over the last years. Several fields of applications can benefit from facial emotion recognition, such as behavior prediction, interpersonal relations, human-computer interactions, recommendation systems. In this work, we develop and analyze an emotion recognition framework based on facial expressions robust to occlusions through the Weber Local Descriptor (WLD). Initially, the occluded facial expressions are reconstructed following an extension approach of Robust Principal Component Analysis (RPCA). Then, WLD features are extracted from the facial expression representation, as well as Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). The feature vector space is reduced using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Finally, K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) classifiers are used to recognize the expressions. Experimental results on three public datasets demonstrated that the WLD representation achieved competitive accuracy rates for occluded and non-occluded facial expressions compared to other approaches available in the literature.

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images.

Face recognition using Weber local descriptors

The human iris texture is one of the most reliable biometric traits because it is unique, and the iris pattern remains stable for years. However, iris images acquired under uncontrolled illumination is one source of difficulties for iris recognition systems, mainly in applications at a distance and in non-cooperative environments. Different levels of light cause iris texture modifications due to pupil size variation. The iris contains 02 groups of muscles: the sphincter pupillae and the dilator pupillae. When the sphincter pupillae contracts the iris reduces the size of the pupil and its texture changes. It is well known in the biometric literature that pupil dilation degrades iris biometric performance. We propose in this paper to evaluate some local texture descriptors for iris recognition, considering pupil contraction and dilation. Furthermore, we propose 02 new texture descriptors called Median-Local-Mapped-Pattern (Median-LMP) and Modified Median-Local-Mapped-Pattern (MM-LMP) and compare their performances to the original Local Mapped Pattern (LMP), the Completed Modeling of Local Binary Pattern (CLBP), the Median Binary Pattern (MBP), the Weber Local Descriptor (WLD) and the Daugman’s method. Our results show that our methodology is more robust when we compare iris samples with different levels of pupil sizes (dilated vs contracted). Besides this, our descriptor performs better than all the compared methods, primarily if one iris with a contracted pupil is used for searching another iris with a dilated pupil.

Histopathological analysis is important for detection of the breast cancer (BC). Computer-aided diagnosis and detection systems are developed to assist the radiologist in the diagnosis process and to relieve the patient from unnecessary pain. In this study, a computer-aided diagnosis system for the early detection of benign and malignant breast cancer is proposed. The proposed system consists of feature extraction, feature ensemble, and classification stages. Various preprocessing steps such as grayscale conversion, noise filtering, and image resizing are employed on the input histopathological images. The local texture descriptors namely Local Binary Pattern (LBP), Frequency Decoded LBP (FDLBP), Binary Gabor Pattern (BGP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF), CENsus TRansform hISTogram (CENTRIST), and Pyramid Histogram of Oriented Gradients (PHOG) are employed for feature extraction from the histopathologic images. The obtained features are then concatenated for the construction of the ensemble of the features. Three classifiers namely Support Vector Machines (SVM), K-nearest neighbor (KNN), and Neural Networks (NN) are used in the detection of the BC and the classification accuracy score is used for performance evaluation. A dataset called BreaKHis is used in the studies. There are 9109 microscopic pictures in BreaKHis, with 2480 benign samples and 5429 malignant samples. During the collection of the data, 82 patients' breast tumor tissues were envisioned using various magnification factors such as 40X, 100X, 200X, and 400X. The accuracy score is used to assess the acquired findings. The results show that the proposed method has the potential to use accurate BC detection.

Segmentation of a river scene is a representative case of complex image segmentation. Different from road segmentation, river scenes often have unstructured boundaries and contain complex light and shadow on the water’s surface. According to the imaging mechanism of water pixels, this paper designed a water description feature based on a multi-block local binary pattern (MB-LBP) and Hue variance in HSI color space to detect the water region in the image. The improved Local Binary Pattern (LBP) feature was used to recognize the water region and the local texture descriptor in HSI color space using Hue variance was used to detect the shadow area of the river surface. Tested on two data sets including simple and complex river scenes, the proposed method has better segmentation performance and consumes less time than those of two other widely used methods.

According to the World Health Organization (WHO) in developing countries around 23% of malignancies are caused by infectious agents. The infection with Helicobac-ter pylori (H. pylori) bacteria seems to be a major cause of stomach cancer (80%). An early diagnosis of gastritis may help decrease the risk of gastric cancer and other complications such as gastric ulcers. The aim of this paper is to evaluate the probability of providing specialists a diagnostic support tool based on computer vision. We used twenty-four endoscopic images of healthy patients and thirty-five images of patients suffering from gastritis to perform an automatic classification process. The suggested approach uses Local Binary Patterns (LBP), texture descriptors, and certain classifiers to perform the automatic classification. The results are promising and show 83% precision in identifying the disease.

The aim of this study is to propose a system that is capable of recognising the identity of a person, indicating whether the person is drunk using only information extracted from thermal face images. The proposed system is divided into two stages, face recognition and classification. In the face recognition stage, test images are recognised using robust face recognition algorithms: Weber local descriptor (WLD) and local binary pattern (LBP). The classification stage uses Fisher linear discriminant to reduce the dimensionality of the features, and those features are classified using a classifier based on a Gaussian mixture model, creating a classification space for each person, extending the state-of-the-art concept of a “DrunkSpace Classifier.” The system was validated using a new drunk person database, which was specially designed for this work. The main results show that the performance of the face recognition stage was 100% with both algorithms, while the drunk identification saw a performance of 86.96%, which is a very promising result considering 46 individuals for our database in comparison with others that can be found in the literature.

Gender recognition for interactive functions becomes essential topic in terms of service robotics applications. Ensemble learning which combines multiple classifiers prediction is now an active area of research in Machine Learning and Pattern Recognition. We propose an ensemble learning to facilitate gender classification. The features which we use are raw data (image pixels as input), Local binary pattern (LBP), Local derivative pattern (LDP), Gabor, and Weber local descriptors (WLD). We carry out comparative experimental studies of various gender recognition schemes, including Eigenfaces, any individual classifiers, Rotation Forest, and Adaboost. Specifically, not only individual classifiers but also ensemble classifiers are based on Support Vector Machine, namely using SVM as component classifier in ensemble learning. Furthermore, we evaluate the effect of image size on classification rate. In conclusion, we find that the best classification rate is achieved with Discrete Adaboost with SVM as component classifier using the aforementioned features. Another finding is that the classification rates will increase when face image size increases.

ECG beat type analysis is important in the detection of various heart diseases. The ECG beats give useful information about the status of the monitored heart condition. Up to now, various artificial intelligence-based methods have been proposed for ECG based heart failure detection. These methods were generally based on either time or frequency domain signal processing routines. In this study, we propose a different approach for ECG beat classification. The proposed approach is based on image processing. Thus, the initial step of the proposed work is converting the ECG beat signals to the ECG beat images. To do that, the ECG beat snapshots are initially saved as ECG beat images and then local feature descriptors are considered for feature extraction from ECG beat images. Eight local feature descriptors namely Local Binary Patterns, Frequency Decoded LBP, Quaternionic Local Ranking Binary Pattern, Binary Gabor Pattern, Local Phase Quantization, Binarized Statistical Image Features, CENsus TRansform hISTogram and Pyramid Histogram of Oriented Gradients are considered for feature extraction. The Support Vector Machines (SVM) classifier is used in the classification stage of the study. Linear, Quadratic, Cubic and Gaussian kernel functions are used in the SVM classifier. Five types of ECG beats from the MIT-BIH arrhythmia dataset are considered in experiments and the classification accuracy is used for performance measure. To construct a balanced training and test sets, 5000 and 10,000 ECG beat samples are randomly selected and are used in experiments in tenfold cross-validation fashion. The obtained results show that the proposed method is quite efficient where the calculated accuracy score is 99.9% and the comparisons with the state-of-the-art method show that the proposed method outperforms other methods.

The use of the ear as a biometric modality has emerged in recent years. It makes it possible to differentiate people thanks to its stability over time and to the richness of its characteristics such as texture, color and size. This paper proposes a novel approach to ear recognition based on a variant of the Local Binary Pattern descriptor called Multi-scale Local Binary Pattern (MLBP). MLBP is calculated locally, by dividing the image into several equal blocks, to extract the ear features which will be used in the matching process to make a decision by detecting the similarities between the feature vectors using City-Block distance (CTB). The proposed method is evaluated on three reference ear databases: IIT Delhi I, IIT Delhi II and USTB-1. The analysis of the results obtained have clearly shown the robustness and the stability of the proposed ear recognition method which is highly competitive, achieving an attractive recognition performances in terms of identification rate at rank-1 up to: 98.40% for IIT Delhi I, 98.64% for IIT Delhi II, and 98.33% for USTB-1.

ABSTRACTPlant diseases have always been a problem because they can significantly decrease both the quality and quantity of crops. Diseases, pests, and weeds present major challenges in crop cultivation, leading to substantial crop damage and posing significant risks to the economy and food security. Plant diseases pose a significant threat to the quality and yield of agricultural products. Prompt and reliable detection and identification of these diseases are crucial for ensuring sustainable agriculture and food security. Preventing ailments and providing guidance to farmers is crucial to enhancing the yield on a large scale. Manual feature extraction is the most expensive approach used in earlier plant disease detection methods. Additionally, many of the real‐time applications face issues with cost complexity, misclassification, and overfitting. Hence, an effective model called Xception‐Deep Kronecker Network (Xception‐DKN) is proposed for severity disease classification utilising hyperspectral leaf image. Initially, the hyperspectral leaf image is pre‐processed. Then, the selection of the band phase is performed utilising Fractional Water Wheel Plant Dingo Optimizer (FWWPDO), that is the incorporation of Dingo Optimizer (DOX), Fractional Calculus (FC), and Water Wheel Plant Algorithm (WWPA). Outputs from the selection bands are forwarded into the leaf segmentation phase that is carried out using Black Hole Entropic Fuzzy Clustering (BHEFC). Next, using a majority voting approach, a fusion of bands is performed. Then, fused band output as well as individual leaf segmentation outcome is exposed into the Feature Extraction (FE) stage for extracting the features, including Weber Local Descriptors (WLDs) and Local Binary Patterns (LBPs). Thereafter, disease recognition is executed on leaves by utilising a Deep Conval Neural Network (deep CNN) for normal and abnormal cases. Nevertheless, Deep CNN hyperparameters are fine‐tuned utilising FWWPDO, which is developed by integrating the Water Wheel Plant Dingo Optimizer (WWPDO) and Fractional Concept (FC). Thereafter, severity level classification is performed using the proposed Xception‐DKN into low, moderate and severe cases. Xception‐DKN is the combined form of Xception and Deep Kronecker Network (DKN), where the layers are adjusted by Taylor concepts. The Xception‐DKN has achieved the highest accuracy of 92.204%, true positive rate (TPR) of 94.011%, and true negative rate (TNR) of 91.210%.

"This paper explores the application of neural networks in multimodal biometric authentication systems, emphasizing the integration of multiple biometric modalities to enhance security, accuracy, and robustness against various attacks. Traditional authentication methods, such as passwords and single-modal biometrics, often suffer from vulnerabilities, including spoofing, environmental factors, and data breaches. To address these challenges, multimodal authentication systems combine several biometric traits, such as facial recognition, fingerprint scanning, voice recognition, and keystroke dynamics, to achieve higher reliability and resistance to security threats. The study provides an overview of public datasets used for training neural network-based biometric authentication models, including VoxCeleb, RAVDESS, MOBIO, and SDUMLA-HMT. These datasets contain diverse biometric information necessary for developing robust multimodal authentication systems. The paper evaluates the effectiveness of existing approaches using key performance metrics such as accuracy, false acceptance rate (FAR), false rejection rate (FRR), and area under the curve (AUC). Additionally, specialized metrics are considered, including failure to enroll rate (FTE), failure to acquire rate (FTA), and template stability (TS), which are crucial for real-world applications. The role of neural networks in multimodal biometric authentication is analyzed by examining state-of-the-art architectures, including convolutional neural networks (CNNs) and deep learning-based feature fusion methods. Various fusion levels—feature-level, score-level, and decision-level—are discussed to determine the optimal integration strategy for improving authentication performance. The results indicate that multimodal systems significantly outperform unimodal authentication methods by reducing vulnerability to spoofing and environmental variations. Experimental findings suggest that integrating multiple biometric traits enhances the system’s adaptability to dynamic conditions, reducing both false acceptance and false rejection rates. Despite these advantages, several challenges remain, including computational complexity, data privacy concerns, and the need for real-time processing capabilities. Future research should focus on optimizing multimodal fusion techniques, improving generalization across different datasets, and enhancing the security of biometric templates against adversarial attacks. Additionally, developing lightweight neural network architectures suitable for mobile and embedded systems is essential for the practical deployment of multimodal authentication technologies"