Enhanced Face Recognition Method Performance on Android vs Windows Platform

Android is becoming one of the most popular operating systems on smartphones, tablet computers and similar mobile devices. With the quick development in mobile device specifications, it is worthy to think about mobile devices as current or - at least - near future replacement of personal computers. This paper presents an enhanced face recognition method. The method is tested on two different platforms using Windows and Android operating systems. This is done to evaluate the method and to compare the platforms. The platforms are compared according to two factors: development simplicity and performance. The target is evaluating the possibility of replacing personal computers using Windows operating system by mobile devices using Android operating system. Face recognition has been chosen because of the relatively high computing cost of image processing and pattern recognition applications comparing with other applications. The experiment results show acceptable performance of the method on Android platform

FedBiometric: Image Features Based Biometric Presentation Attack Detection Using Hybrid CNNs-SVM in Federated Learning

In the past few years, biometric identification systems have become popular for personal, national, and global security. In addition to other biometric modalities, facial and fingerprint recognition have gained popularity due to their uniqueness, stability, convenience, and cost-effectiveness compared to other biometric modalities. However, the evolution of fake biometrics, such as printed materials, 2D or 3D faces, makeup, and cosmetics, has brought new challenges. As a result of these modifications, several facial and fingerprint Presentation Attack Detection methods have been proposed to distinguish between live and spoof faces or fingerprints. Federated learning can play a significant role in this problem due to its distributed learning setting and privacy-preserving advantages. This work proposes a hybrid ResNet50-SVM based federated learning model for facial Presentation Attack Detection utilizing Local Binary Pattern (LBP), or Gabor filter-based extracted image features. For fingerprint Presentation Attack Detection (PAD), this work proposes a hybrid CNN-SVM based federated learning model utilizing Local Binary Pattern (LBP), or Histograms of Oriented Gradient (HOG)-based extracted image features

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

The computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment

Application of Texture Descriptors to Facial Emotion Recognition in Infants

The recognition of facial emotions is an important issue in computer vision and artificial intelligence due to its important academic and commercial potential. If we focus on the health sector, the ability to detect and control patients’ emotions, mainly pain, is a fundamental objective within any medical service. Nowadays, the evaluation of pain in patients depends mainly on the continuous monitoring of the medical staff when the patient is unable to express verbally his/her experience of pain, as is the case of patients under sedation or babies. Therefore, it is necessary to provide alternative methods for its evaluation and detection. Facial expressions can be considered as a valid indicator of a person’s degree of pain. Consequently, this paper presents a monitoring system for babies that uses an automatic pain detection system by means of image analysis. This system could be accessed through wearable or mobile devices. To do this, this paper makes use of three different texture descriptors for pain detection: Local Binary Patterns, Local Ternary Patterns, and Radon Barcodes. These descriptors are used together with Support Vector Machines (SVM) for their classification. The experimental results show that the proposed features give a very promising classification accuracy of around 95% for the Infant COPE database, which proves the validity of the proposed method.This work has been partially supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) under project CloudDriver4Industry TIN2017-89266-R, and by the Conselleria de Educación, Investigación, Cultura y Deporte, of the Community of Valencia, Spain, within the program of support for research under project AICO/2017/134

Smart home Management System with Face Recognition Based on ArcFace Model in Deep Convolutional Neural Network

In recent years, artificial intelligence has proved its potential in many fields, especially in computer vision. Facial recognition is one of the most essential tasks in the field of computer vision with various prospective applications from academic research to intelligence service. In this paper, we propose an efficient deep learning approach to facial recognition. Our approach utilizes the architecture of ArcFace model based on the backbone MobileNet V2, in deep convolutional neural network (DCNN). Assistive techniques to increase highly distinguishing features in facial recognition. With the supports of the facial authentication combines with hand gestures recognition, users will be able to monitor and control his home through his mobile phone/tablet/PC. Moreover, they communicate with data and connect to smart devices easily through IoT technology. The overall proposed model is 97% of accuracy and a processing speed of 25 FPS. The interface of the smart home demonstrates the successful functions of real-time operations

Analysis of the Efficacy of Real-Time Hand Gesture Detection with Hog and Haar-Like Features Using SVM Classification

The field of hand gesture recognition has recently reached new heights thanks to its widespread use in domains like remote sensing, robotic control, and smart home appliances, among others. Despite this, identifying gestures is difficult because of the intransigent features of the human hand, which make the codes used to decode them illegible and impossible to compare. Differentiating regional patterns is the job of pattern recognition. Pattern recognition is at the heart of sign language. People who are deaf or mute may understand the spoken language of the rest of the world by learning sign language. Any part of the body may be used to create signs in sign language. The suggested system employs a gesture recognition system trained on Indian sign language. The methods of preprocessing, hand segmentation, feature extraction, gesture identification, and classification of hand gestures are discussed in this work as they pertain to hand gesture sign language. A hybrid approach is used to extract the features, which combines the usage of Haar-like features with the application of Histogram of Oriented Gradients (HOG).The SVM classifier is then fed the characteristics it has extracted from the pictures in order to make an accurate classification. A false rejection error rate of 8% is achieved while the accuracy of hand gesture detection is improved by 93.5%