Person Verification Based on Multimodal Biometric Recognition

Nowadays, person recognition has received significant attention due to broad applications in the security system. However, most person recognition systems are implemented based on unimodal biometrics such as face recognition or voice recognition. Biometric systems that adopted unimodal have limitations, mainly when the data contains outliers and corrupted datasets. Multimodal biometric systems grab researchers’ consideration due to their superiority, such as better security than the unimodal biometric system and outstanding recognition efficiency. Therefore, the multimodal biometric system based on face and fingerprint recognition is developed in this paper. First, the multimodal biometric person recognition system is developed based on Convolutional Neural Network (CNN) and ORB (Oriented FAST and Rotated BRIEF) algorithm. Next, two features are fused by using match score level fusion based on Weighted Sum-Rule. The verification process is matched if the fusion score is greater than the pre-set threshold. The algorithm is extensively evaluated on UCI Machine Learning Repository Database datasets, including one real dataset with state-of-the-art approaches. The proposed method achieves a promising result in the person recognition system.

Person Recognition Using Soft Biometric

Global security concerns raised the multiplication of video surveillance devices. Intelligent systems identify the person captured at the different cameras, angles, views, background, and wearing different accessories. Gait is measured at a distance without human cooperation. In this work, gait recognition is increased by concatenating semantic features with traditional features. Combining features are dimensionally reduced and classified using classifiers. This method motivates in future to increase the gait recognition with less false positive detection.

YÜZ TANIMA TABANLI DUYURU SİSTEMİ TASARIMI VE KULLANILABİLİRLİĞİ

The rapid developments in technology have an increasing impact and use on biometric person recognition systems. Facial recognition-based systems, one of the biometric person recognition systems, have been widely used in recent years thanks to their easy implementation, fast integration and simple usage as they do not require any additional equipment. Especially the widespread use of computer vision and cloud-computing based applications, smart face recognition systems have become an indispensable part of our lives in recent years. The use of these systems, which have become widespread in security, health, education, military, shopping mall and industrial areas, has increased more during the pandemic period. Institutions and organizations do not want to allocate time and cost to write their own software for face recognition based systems. The services offered by major cloud computing providers can be used to solve this problem. In this context, the article presents a smart announcement system design using cloud computing based face recognition technology. In the past, making an announcement has been seen as a difficult task. It was thought to be a time consuming task, both because of the cost of printing and because all the operations had to be repeated when there were changes in the announcement. Today, signs have left their places to digital screens. It will especially ensure that announcements, warnings, promotions, and notifications are performed effectively at the developed system for large scale institutions, organizations, factories, universities, shopping malls and health institutions. Facial recognition based smart announcement system detects features such as person recognition, gender, and age estimation at a rate of 100% and displays personal announcements according to their priority status. In addition, according to the experimental studies, it was observed that the person recognition and the presentation of the announcements on the screen took an average of 1.3 seconds. According to the announcement system survey, 85% of those who use the system stated that it is useful and user-friendly.

Handwritten Character Recognition Using Neural Network

In this paper, an enterprise is made to perceive manually written characters for English letters so as. The precept point of this mission is to plan a master framework for, "HCR(English) utilizing neural community". That could viably understand a particular individual-of-kind layout making use of the artificial neural community approach. The manually written man or woman acknowledgment trouble has grown to be the maximum famous trouble in ai. Handwritten man or woman acknowledgment has been a difficult space of exam, with the execution of gadgets getting to know we suggest a neural network-based methodology. The development is based totally on neural network, that is a subject of look at in artificial intelligence. Distinct strategies and methods are used to broaden a handwriting person recognition system. Acknowledgment, precision fee, execution, and execution time are massive versions on the way to be met through the technique being applied. The purpose is to illustrate the effectiveness of neural networks for handwriting character recognition.