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
    Improving the accuracy of automated facial age estimation to aid CSEM investigations
    The investigation of violent crimes against individuals, such as the investigation of child sexual exploitation material (CSEM), is one of the more commonly encountered criminal investigation types throughout the world. While hash lists of known CSEM content are commonly used to identify previously encountered material on suspects’ devices, previously unencountered material requires expert, manual analysis and categorisation. The discovery, analysis, and categorisation of these digital images and videos has the potential to be significantly expedited with the use of automated artificial intelligence (AI) based techniques. Intelligent, automated evidence processing and prioritisation has the potential to aid investigators in alleviating some of the digital evidence backlogs that have become commonplace worldwide. In order for AI-aided CSEM investigations to be beneficial, the fundamental question when analysing multimedia content becomes “how old is each subject encountered?’’. Our work presents the evaluation of existing cloud-based and offline age estimation services, introduces our deep learning model, DS13K, which was created with a VGG-16 Deep Convolutional Neural Network (CNN) architecture, and develops an ensemble technique that improves the accuracy of underage facial age estimation. In addition to our model, a number of existing services including Amazon Rekognition, Microsoft Azure Cognitive Services, How-Old.net, and Deep Expectation (DEX) were used to create an ensemble learning technique. It was found that for the borderline adulthood age range (i.e., 16–17 years old), our DS13K model substantially outperformed existing services, achieving a performance accuracy of 68%. A comparative examination of the obtained results allowed us to identify performance trends and issues inherent to each service/tool and develop ensemble techniques to improve the accuracy of automated adulthood determination.
      17
  • 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