Polar Fusion Technique Analysis for Evaluating the Performances of Image Fusion of Thermal and Visual Images for Human Face Recognition

This paper presents a comparative study of two different methods, which are based on fusion and polar transformation of visual and thermal images. Here, investigation is done to handle the challenges of face recognition, which include pose variations, changes in facial expression, partial occlusions, variations in illumination, rotation through different angles, change in scale etc. To overcome these obstacles we have implemented and thoroughly examined two different fusion techniques through rigorous experimentation. In the first method log-polar transformation is applied to the fused images obtained after fusion of visual and thermal images whereas in second method fusion is applied on log-polar transformed individual visual and thermal images. After this step, which is thus obtained in one form or another, Principal Component Analysis (PCA) is applied to reduce dimension of the fused images. Log-polar transformed images are capable of handling complicacies introduced by scaling and rotation. The main objective of employing fusion is to produce a fused image that provides more detailed and reliable information, which is capable to overcome the drawbacks present in the individual visual and thermal face images. Finally, those reduced fused images are classified using a multilayer perceptron neural network. The database used for the experiments conducted here is Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark thermal and visual face images. The second method has shown better performance, which is 95.71% (maximum) and on an average 93.81% as correct recognition rate.Comment: Proceedings of IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (IEEE CIBIM 2011), Paris, France, April 11 - 15, 201

Implementation of the Convolutional Neural Network Method to Detect the Use of Masks

The planet has been taken seriously by Coronavirus disease since the end of 2019. Wearing a mask in public is one of the key means of security for people. Furthermore, certain public service vendors only require clients to use the service if they wear masks correctly. However, based on image processing, there is relatively little study into the discovery of face masks. Almost everybody appears to wear a mask in order to shield themselves from the COVID-19 Pandemic. Monitoring whether people in the crowd wear face masks at the most public place, such as malls, museums, parks, has become increasingly important. The development of an AI approach to deal with if the person wears a face mask and their entrance would significantly assist society. In this article, we will use a deep learning model that is then combined with Keras / TensorFlow & OpenCV, respectively CNN or Confusional Neural Network. The accuracy of the research results obtained from this model is more than 96%

New Finger Biometric Method Using Near Infrared Imaging

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%

Facial Landmark Detection and Estimation under Various Expressions and Occlusions

Landmark localization is one of the fundamental approaches to facial expressions recognition, occlusions detection and face alignments. It plays a vital role in many applications in image processing and computer vision. The acquisition conditions such as expression, occlusion and background complexity affect the landmark localization performance, which subsequently lead to system failure. In this paper, the writers bestowed the challenges of various landmark detection techniques, number of landmark points and dataset types been employed from the existing literatures. However, advance technique for facial landmark detection under various expressions and occlusions was presented. This was carried out using Point Distribution Model (PDM) to estimate the occluded part of the facial regions and detect the face. The proposed method was evaluated using University Milano Bicocca Database (UMB). This approach gave more promising result when compared to several previous works. In conclusion, the technique detected images despite varieties of occlusions and expressions. It can further be applied on images with different poses and illumination variations

Masked face recognition using deep learning: a review

A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR