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Full-Text Articles in Physical Sciences and Mathematics
An Ml Based Digital Forensics Software For Triage Analysis Through Face Recognition, Gaurav Gogia, Parag H. Rughani
An Ml Based Digital Forensics Software For Triage Analysis Through Face Recognition, Gaurav Gogia, Parag H. Rughani
Journal of Digital Forensics, Security and Law
Since the past few years, the complexity and heterogeneity of digital crimes has increased exponentially, which has made the digital evidence & digital forensics paramount for both criminal investigation and civil litigation cases. Some of the routine digital forensic analysis tasks are cumbersome and can increase the number of pending cases especially when there is a shortage of domain experts. While the work is not very complex, the sheer scale can be taxing. With the current scenarios and future predictions, crimes are only going to become more complex and the precedent of collecting and examining digital evidence is only going …
Measuring The Relationship Of Gender Misclassification And Automated Face Recognition Match Accuracy Relative To Skin Tone, Afi Edem-Edi Gbekevi
Measuring The Relationship Of Gender Misclassification And Automated Face Recognition Match Accuracy Relative To Skin Tone, Afi Edem-Edi Gbekevi
Theses and Dissertations
The gap of accuracy observed in some commercial face analytic systems based on race and gender raised questions about the equity and fairness of those systems. Since these systems are part of several applications today, some more critical than others, it urges designers to detect and mitigate any sources of bias. In this thesis, we begin by clarifying the confusion between face analytic, face recognition, and face processing systems. Then, we analyze gender classification accuracy using two datasets and three classifiers. The Pilot Parliaments Benchmark dataset is examined with an open-source algorithm to corroborate the gender shade. Secondly, the Morph …
Lightweight Privacy-Preserving Ensemble Classification For Face Recognition, Zhuo Ma, Yang Liu, Ximeng Liu, Jianfeng Ma, Kui Ren
Lightweight Privacy-Preserving Ensemble Classification For Face Recognition, Zhuo Ma, Yang Liu, Ximeng Liu, Jianfeng Ma, Kui Ren
Research Collection School Of Computing and Information Systems
The development of machine learning technology and visual sensors is promoting the wider applications of face recognition into our daily life. However, if the face features in the servers are abused by the adversary, our privacy and wealth can be faced with great threat. Many security experts have pointed out that, by 3-D-printing technology, the adversary can utilize the leaked face feature data to masquerade others and break the E-bank accounts. Therefore, in this paper, we propose a lightweight privacy-preserving adaptive boosting (AdaBoost) classification framework for face recognition (POR) based on the additive secret sharing and edge computing. First, we …
Evaluation And Understandability Of Face Image Quality Assessment, Mohammad I. Nouyed
Evaluation And Understandability Of Face Image Quality Assessment, Mohammad I. Nouyed
Graduate Theses, Dissertations, and Problem Reports
Face image quality assessment (FIQA) has been an area of interest to researchers as a way to improve the face recognition accuracy. By filtering out the low quality images we can reduce various difficulties faced in unconstrained face recognition, such as, failure in face or facial landmark detection or low presence of useful facial information. In last decade or so, researchers have proposed different methods to assess the face image quality, spanning from fusion of quality measures to using learning based methods. Different approaches have their own strength and weaknesses. But, it is hard to perform a comparative assessment of …
Facepet: Enhancing Bystanders' Facial Privacy With Smart Wearables/Internet Of Things, Alfredo J. Perez, Sherali Zeadally, Luis Y. Matos Garcia, Jaouad A. Mouloud, Scott Griffith
Facepet: Enhancing Bystanders' Facial Privacy With Smart Wearables/Internet Of Things, Alfredo J. Perez, Sherali Zeadally, Luis Y. Matos Garcia, Jaouad A. Mouloud, Scott Griffith
Information Science Faculty Publications
Given the availability of cameras in mobile phones, drones and Internet-connected devices, facial privacy has become an area of major interest in the last few years, especially when photos are captured and can be used to identify bystanders’ faces who may have not given consent for these photos to be taken and be identified. Some solutions to protect facial privacy in photos currently exist. However, many of these solutions do not give a choice to bystanders because they rely on algorithms that de-identify photos or protocols to deactivate devices and systems not controlled by bystanders, thereby being dependent on the …