IMPROVING DEEP-LEARNING-BASED FACE RECOGNITION TO INCREASE ROBUSTNESS AGAINST MORPHING ATTACKS
Una M. Kelly, Luuk Spreeuwers and Raymond Veldhuis University of Twente, The Netherlands
State-of-the-art face recognition systems (FRS) are vulnerable to morphing attacks, in which two photos of different people are merged in such a way that the resulting photo resembles both people. Such a photo could be used to apply for a passport, allowing both people to travel with the same identity document. Research has so far focussed on developing morphing detection methods. We suggest that it might instead be worthwhile to make face recognition systems themselves more robust to morphing attacks. We show that deep-learning-based face recognition can be improved simply by treating morphed images just like real images during training but also that, for significant improvements, more work is needed. Furthermore, we test the performance of our FRS on morphs of a type not seen during training. This addresses the problem of overfitting to the type of morphs used during training, which is often overlooked in current research.
Biometrics, Morphing Attack Detection, Face Recognition, Vulnerability of Biometric Systems.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101901.pdf
Volume Link :
http://airccse.org/csit/V10N19.html
NEW ALGORITHMS FOR COMPUTING FIELD OF VISION OVER 2D GRIDS
Evan R.M. Debenham and Roberto Solis-Oba The University of Western Ontario, Canada
The aim of this paper is to propose new algorithms for Field of Vision (FOV) computation which improve on existing work at high resolutions. FOV refers to the set of locations that are visible from a specific position in a scene of a computer game.
Field of Vision (FOV), Computer Games, Visibility Determination, Algorithms.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101801.pdf
Volume Link :
http://airccse.org/csit/V10N18.html
MINIMUM VIABLE MODEL ESTIMATES FOR MACHINE LEARNING PROJECTS
John Hawkins Transitional AI Research Group, Sydney, Australia
Prioritization of machine learning projects requires estimates of both the potential ROI of the business case and the technical difficulty of building a model with the required characteristics. In this work we present a technique for estimating the minimum required performance characteristics of a predictive model given a set of information about how it will be used. This technique will result in robust, objective comparisons between potential projects. The resulting estimates will allow data scientists and managers to evaluate whether a proposed machine learning project is likely to succeed before any modelling needs to be done. The technique has been implemented into the open source application MinViME (Minimum Viable Model Estimator) which can be installed via the PyPI python package management system, or downloaded directly from the GitHub repository. Available at https://github.com/john-hawkins/MinViME.
Machine Learning, ROI Estimation, Machine Learning Metrics, Cost Sensitive Learning
For More Details :
https://aircconline.com/csit/papers/vol10/csit101803.pdf
Volume Link :
http://airccse.org/csit/V10N18.html
MACHINE LEARNING ALGORITHM FOR NLOS MILLIMETER WAVE IN 5G V2X COMMUNICATION
Deepika Mohan1 , G. G. Md. Nawaz Ali2 and Peter Han Joo Chong1 1Auckland University of Technology, New Zealand, 2University of Charleston, USA
The 5G vehicle-to-everything (V2X) communication for autonomous and semi-autonomous driving utilizes the wireless technology for communication and the Millimeter Wave bands are widely implemented in this kind of vehicular network application. The main purpose of this paper is to broadcast the messages from the mmWave Base Station to vehicles at LOS (Line-ofsight) and NLOS (Non-LOS). Relay using Machine Learning (RML) algorithm is formulated to train the mmBS for identifying the blockages within its coverage area and broadcast the messages to the vehicles at NLOS using a LOS nodes as a relay. The transmission of information is faster with higher throughput and it covers a wider bandwidth which is reused, therefore when performing machine learning within the coverage area of mmBS most of the vehicles in NLOS can be benefited. A unique method of relay mechanism combined with machine learning is proposed to communicate with mobile nodes at NLOS.
5G, Millimeter Wave, Machine Learning, Relay, V2X communication.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101706.pdf
Volume Link :
http://airccse.org/csit/V10N17.html
PREDICTING FAILURES OF MOLTENO AND BAERVELDT GLAUCOMA DRAINAGE DEVICES USING MACHINE LEARNING MODELS
Paul Morrison1, Maxwell Dixon2, Arsham Sheybani2 , Bahareh Rahmani1,3 1Fontbonne University,St. Louis, MO, 2Washington University, St. Louis, MO 3Maryville University, , MO
The purpose of this retrospective study is to measure machine learning models' ability to predict glaucoma drainage device (GDD) failure based on demographic information and preoperative measurements. The medical records of sixty-two patients were used. Potential predictors included the patient's race, age, sex, preoperative intraocular pressure (IOP), preoperative visual acuity, number of IOP-lowering medications, and number and type of previous ophthalmic surgeries. Failure was defined as final IOP greater than 18 mm Hg, reduction in IOP less than 20% from baseline, or need for reoperation unrelated to normal implant maintenance. Five classifiers were compared: logistic regression, artificial neural network, random forest, decision tree, and support vector machine. Recursive feature elimination was used to shrink the number of predictors and grid search was used to choose hyperparameters. To prevent leakage, nested cross-validation was used throughout. Overall, the best classifier was logistic regression. With a small amount of data, the best classifier was logistic regression, but with more data, the best classifier was the random forest. All five classification methods discussed at this research confirm that race effects on failure glaucoma drainage. Use of topical beta-blockers preoperatively is related to device failure. In treating glaucoma medically, prostaglandin equivalents are often first-line with beta-blockers used second-line or as a reasonable alternative first-line agent.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101610.pdf
Volume Link :
http://airccse.org/csit/V10N16.html
A PREDICTIVE MODEL FOR KIDNEY TRANSPLANT GRAFT SURVIVAL USING MACHINE LEARNING
Eric S. Pahl1 , W. Nick Street2 , Hans J. Johnson3 and Alan I. Reed4, 1,2,3,4University of Iowa, Iowa, USA
Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p) .
Kidney Transplant, Decision Support, Random Forest, Health Informatics, Clinical Decision Making, Machine Learning & Survival Analysis
For More Details :
https://aircconline.com/csit/papers/vol10/csit101609.pdf
Volume Link :
http://airccse.org/csit/V10N16.html
ARTIST, STYLE AND YEAR CLASSIFICATION USING FACE RECOGNITION AND CLUSTERING WITH CONVOLUTIONAL NEURAL NETWORKS
Doruk Pancaroglu STM A.S., Ankara, Turkey
Artist, year and style classification of fine-art paintings are generally achieved using standard image classification methods, image segmentation, or more recently, convolutional neural networks (CNNs). This works aims to use newly developed face recognition methods such as FaceNet that use CNNs to cluster fine-art paintings using the extracted faces in the paintings, which are found abundantly. A dataset consisting of over 80,000 paintings from over 1000 artists is chosen, and three separate face recognition and clustering tasks are performed. The produced clusters are analyzed by the file names of the paintings and the clusters are named by their majority artist, year range, and style. The clusters are further analyzed and their performance metrics are calculated. The study shows promising results as the artist, year, and styles are clustered with an accuracy of 58.8, 63.7, and 81.3 percent, while the clusters have an average purity of 63.1, 72.4, and 85.9 percent.
Face Recognition, Clustering, Convolutional Neural Networks, Art Identification.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101604.pdf
Volume Link :
http://airccse.org/csit/V10N16.html
DATA PREDICTION OF DEFLECTION BASIN EVOLUTION OF ASPHALT PAVEMENT STRUCTURE BASED ON MULTI-LEVEL NEURAL NETWORK
Shaosheng Xu1 , Jinde Cao2 and Xiangnan Liu2 1,2Southeast University, Nanjing, China
Aiming at reducing the high cost of test data collection of deflection basins in the structural design of asphalt pavement and shortening the long test time of new structures, this paper innovatively designs a structure coding network based on traditional neural networks to map the pavement structure to an abstract space. Therefore, the generalization ability of the neural network structure is improved, and a new multi-level neural network model is formed to predict the evolution data of the deflection basin of the untested structure. By testing the experimental data of RIOHTRACK, the network structure predicts the deflection basin data of untested pavement structure, of which the average prediction error is less than 5%. .
multi-level neural network, Encoding converter, structural of asphalt pavement, deflection basins, RIOHTRACK.
For More Details :
https://aircconline.com/csit/papers/vol10/csit101304.pdf
Volume Link :
http://airccse.org/csit/V10N13.html
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