Now showing 1 - 4 of 4
  • Publication
    Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning
    Achieving high performance for facial age estimation with subjects in the borderline between adulthood and non-adulthood has always been a challenge. Several studies have used different approaches from the age of a baby to an elder adult and different datasets have been employed to measure the mean absolute error (MAE) ranging between 1.47 to 8 years. The weakness of the algorithms specifically in the borderline has been a motivation for this paper. In our approach, we have developed an ensemble technique that improves the accuracy of underage estimation in conjunction with our deep learning model (DS13K) that has been fine-tuned on the Deep Expectation (DEX) model. We have achieved an accuracy of 68% for the age group 16 to 17 years old, which is 4 times better than the DEX accuracy for such age range. We also present an evaluation of existing cloud-based and offline facial age prediction services, such as Amazon Rekognition, Microsoft Azure Cognitive Services, How-Old.net and DEX.
    Scopus© Citations 12  283
  • Publication
    Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation
    Swift response to the detection of endangered minors is an ongoing concern for law enforcement. Many child-focused investigations hinge on digital evidence discovery and analysis. Automated age estimation techniques are needed to aid in these investigations to expedite this evidence discovery process, and decrease investigator exposure to traumatic material. Automated techniques also show promise in decreasing the overflowing backlog of evidence obtained from increasing numbers of devices and online services. A lack of sufficient training data combined with natural human variance has been long hindering accurate automated age estimation - especially for underage subjects. This paper presented a comprehensive evaluation of the performance of two cloud age estimation services (Amazon Web Service's Rekognition service and Microsoft Azure's Face API) against a dataset of over 21,800 underage subjects. The objective of this work is to evaluate the influence that certain human biometric factors, facial expressions, and image quality (i.e. blur, noise, exposure and resolution) have on the outcome of automated age estimation services. A thorough evaluation allows us to identify the most influential factors to be overcome in future age estimation systems.
      40Scopus© Citations 4
  • Publication
    An Evaluation of Google Plus Communities as an Active Learning Journal Alternative to Improve Learning Efficacy
    (ICEP, 2015-12-04) ;
    Learning journals are a very beneficial learning tool for students across a range of disciplines. The requirement of frequent entries to a journal encourages students to start achieving the learning objectives from the first week of a module. The completed journal serves as a useful revision resource for students preparing for a final exam or even long after the module’s completion. The downside to learning journals is that they are passive and the class as a whole does not benefit from the variety of opinions, articles and personal experiences logged in their classmates' journals. If the journal is only handed in at the end a semester, there is no room for feedback for the students on their entries until after the module has completed. In this paper, guidelines for the deployment of an active learning journal alternative, using Google Plus Communities, are presented. A literature review is also included for alternative case studies in using learning journals, weblogs, and wikis for recording and encouraging student learning throughout a module.
      204
  • Publication
    Current Challenges and Future Research Areas for Digital Forensic Investigation
    Given the ever-increasing prevalence of technology in modern life, there is a corresponding increase in the likelihood of digital devices being pertinent to a criminal investigation or civil litigation. As a direct consequence, the number of investigations requiring digital forensic expertise is resulting in huge digital evidence backlogs being encountered by law enforcement agencies throughout the world. It can be anticipated that the number of cases requiring digital forensic analysis will greatly increase in the future. It is also likely that each case will require the analysis of an increasing number of devices including computers, smartphones, tablets, cloud-based services, Internet of Things devices, wearables, etc. The variety of new digital evidence sources poses new and challenging problems for the digital investigator from an identification, acquisition, storage and analysis perspective. This paper explores the current challenges contributing to the backlog in digital forensics from a technical standpoint and outlines a number of future research topics that could greatly contribute to a more efficient digital forensic process.
      592