Bi-Gaussian score equalization in an audio-visual SVM-based person verification system

In multimodal fusion systems a normalization of the features or the scores is needed before the fusion process. In this work, in addition to the conventional methods, histogram equalization, which was recently introduced by the authors in multimodal systems, and Bi-Gaussian equalization, which takes into account the separate statistics of the genuine and impostor scores, and is introduced in this paper, are applied upon the scores in a multimodal SVM-based person verification system composed by prosodic, speech spectrum, and face information. Bi-Gaussian equalization has obtained the best results and outperform in more than a 23.25% the results obtained by Min-Max normalization.Postprint (published version

Achieving Information Security by multi-Modal Iris-Retina Biometric Approach Using Improved Mask R-CNN

The need for reliable user recognition (identification/authentication) techniques has grown in response to heightened security concerns and accelerated advances in networking, communication, and mobility. Biometrics, defined as the science of recognizing an individual based on his or her physical or behavioral characteristics, is gaining recognition as a method for determining an individual\u27s identity. Various commercial, civilian, and forensic applications now use biometric systems to establish identity. The purpose of this paper is to design an efficient multimodal biometric system based on iris and retinal features to assure accurate human recognition and improve the accuracy of recognition using deep learning techniques. Deep learning models were tested using retinographies and iris images acquired from the MESSIDOR and CASIA-IrisV1 databases for the same person. The Iris region was segmented from the image using the custom Mask R-CNN method, and the unique blood vessels were segmented from retinal images of the same person using principal curvature. Then, in order to aid precise recognition, they optimally extract significant information from the segmented images of the iris and retina. The suggested model attained 98% accuracy, 98.1% recall, and 98.1% precision. It has been discovered that using a custom Mask R-CNN approach on Iris-Retina images improves efficiency and accuracy in person recognition

A review of finger vein recognition system

Recently, the security-based system using finger vein as a biometric trait has been getting more attention from researchers all over the world, and these researchers have achieved positive progress. Many works have been done in different methods to improve the performance and accuracy of the personal identification and verification results. This paper discusses the previous methods of finger vein recognition system which include three main stages: preprocessing, feature extraction and classification. The advantages and limitations of these previous methods are reviewed at the same time we present the main problems of the finger vein recognition system to make it as a future direction in this field

Machine Learning Technique and Normalization Cross Correlation Model Applied for Face Recognition

Face recognition systems just like any other biometric systems have continued to stand the test of time as a reliable means of human verification and identification. The high rate of fraud, crime, and terrorism in Nigeria and the world at large makes it increasingly necessary to have recognition systems that will be compatible with security devices currently deployed. However, the accuracy of facial recognition system is dependent on the adequacy of the model applied. This work applies a combination of Support Vector Machine (SVM) and Normalization Cross Correlation (NCC) starting with a preprocessing stage that involves filtering, cropping, normalization as well as histogram equalization of the face images. The facial images were trained and classified using Support Vector Machine then verified by NCC. The experimental study of the model with benchmarked face images showed that the model is very suitable for obtaining a better accuracy level. The False Acceptance Rate (FAR), False Rejection Rate (FRR), Genuine Acceptance Rate (GAR) and Total Error Rate (TER) values established the superiority of the proposed model over some related ones

Biometric Face Recognition Based on Enhanced Histogram Approach

Biometric face recognition including digital processing and analyzing a subject's facial structure. This system has a certain number of points and measures, including the distances between the main features such as eyes, nose and mouth, angles of features such as the jaw and forehead with the lengths of the different parts of the face. With this information, the implemented algorithm creates a unique model with all the digital data. This model can then be compared with the huge databases of images of the face to identify the subject. The recognition features are retrieved here using histogram equalization technique. A high-resolution result is obtained applying this algorithm under the conditions of a specific image database.