Features and statistical classifiers for face image analysis

This thesis presents the systematic analysis of feature spaces and classification schemes for face image processing. Linear discriminants, probabilistic classifiers, and nearest neighbour classifiers are applied to face/nonface classification in various feature spaces including original grayscale space, face-image-whitened space, anything-image-whitened space, and double-whitened space. According to the classification error rates, the probabilistic classifiers performed the best, followed by nearest neighbour classifiers, and then the linear discriminant classifier. However, the former two kinds of classifiers are more computationally demanding. No matter what kind of classifier is used, the whitened space with reduced dimensionality improves classification performance. -- A new feature extraction technique, named dominant feature extraction, is invented and applied to face/nonface classification with encouraging results. This technique extracts the features corresponding to the mean-difference and variance-difference of two classes. Other classification schemes, including the repeated Fisher's Linear Discriminant (FLD) and a moving-centre scheme, are newly proposed and tested. The Maximum Likelihood (ML) classifier based on hyperellipsoid distribution is applied for the first time to face/nonface classification. -- Face images are conventionally represented by grayscales. This work presents a new representation that includes motion vectors, obtained through optical flow analysis between an input image and a neutral template, and a deformation residue that is the difference between the deformed input image and the template. The face images compose a convex cluster in this representation space. The viability of this space is tested and demonstrated through classification experiments on face detection, expression analysis, pose estimation, and face recognition. When the FLD is applied to face/nonface classification and smiling/nonsmiling face classification, the new representation of face images outperforms the traditional grayscale representation. Face recognition experiments using the nearest neighbour classifier on the Olivetti and Oracle Research Laboratory (ORL) face database shows that the deformation residue representation is superior to all other representations. These promising results demonstrate that as a general-purpose space, the derived representation space is suitable for almost all aspects of face image processing

Machine learning in orthopedics: a literature review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles\u2019 content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

Algorithmic Reason

Are algorithms ruling the world today? Is artificial intelligence making life-and-death decisions? Are social media companies able to manipulate elections? As we are confronted with public and academic anxieties about unprecedented changes, this book offers a different analytical prism to investigate these transformations as more mundane and fraught. Aradau and Blanke develop conceptual and methodological tools to understand how algorithmic operations shape the government of self and other. While disperse and messy, these operations are held together by an ascendant algorithmic reason. Through a global perspective on algorithmic operations, the book helps us understand how algorithmic reason redraws boundaries and reconfigures differences. The book explores the emergence of algorithmic reason through rationalities, materializations, and interventions. It traces how algorithmic rationalities of decomposition, recomposition, and partitioning are materialized in the construction of dangerous others, the power of platforms, and the production of economic value. The book shows how political interventions to make algorithms governable encounter friction, refusal, and resistance. The theoretical perspective on algorithmic reason is developed through qualitative and digital methods to investigate scenes and controversies that range from mass surveillance and the Cambridge Analytica scandal in the UK to predictive policing in the US, and from the use of facial recognition in China and drone targeting in Pakistan to the regulation of hate speech in Germany. Algorithmic Reason offers an alternative to dystopia and despair through a transdisciplinary approach made possible by the authors’ backgrounds, which span the humanities, social sciences, and computer sciences