Face recognition is a biometric technique that can be used for a variety of purposes, such as national security, access control, identity fraud, banking, and finding missing children. Faces are highly dynamic and facial features are not always easily extracted, which can lead to discarding textural information like the smoothness of faces, a hairstyle that, might contain strong identity information. In addition, brightness, scale, and facial expressions play a significant role in the face-recognizing process. Therefore, face recognition is considered as a difficult problem. To figure out this problem effective methods using databases techniques are needed. This paper describes face recognition methods and their structure. Based on Wen Yi Zhao and Rama Chellappa work the face recognition methods are divided into three groups: a holistic approach, feature-based approach, and hybrid approach, where Principal Component Analysis PCA, a holistic approach method, is presented as a mathematical technique that can assist the process of face recognition. Also, the paper shows how the PCA is used to extract facial features by removing the principal components of the available multidimensional data.

Understanding diversity is important in our inclusive society to hedge against ignorance and accommodate plural perspectives. Diversity nowadays can be observed in online social spaces. People from different backgrounds (e.g. gender, age, culture, expertise) are interacting every day around online digital objects (e.g. videos, images and web articles) leaving their social content in different format, commonly as textual comments and profiles. The social clouds around digital objects (i.e. user comments, user profiles and other metadata of digital objects) offer rich source of information about the users and their perspectives on different domains. Although, researchers from disparate disciplines have been working on understanding and measuring diversity from different perspectives, little has been done to automatically measure diversity in social clouds. This is the main objective of this research. This research proposes a semantic driven computational model to systematically represent and automatically measure diversity in a social cloud. Definitions from a prominent diversity framework and Semantic Web techniques underpin the proposed model. Diversity is measured based on four diversity indices - variety, balance, coverage and (within and across) disparity with regards to two perspectives – (a) domain, which is captured in user comments and represented by domain ontologies, and (b) user, which is captured in profiles of users who made the comments and represented by a proposed User Diversity Ontology. The proposed model is operationalised resulting in a Semantic Driven Diversity Analytics Tool (SeDDAT), which is responsible for diversity profiling based on the diversity indices. The proposed approach of applying the model is illustrated on social clouds from two social spaces - open (YouTube) and closed (Active Video Watching (AVW-Space)). The open social cloud shows the applicability of the model to generate diversity profiles of a large pool of videos (600) with thousands of users and comments. Closed social clouds of two user groups around same set of videos illustrate transferability and further utility of the model. A list of possible diversity patterns within social clouds is provided, which in turn deepen the understanding of diversity and open doors for further utilities of the diversity profiles. The proposed model is applicable in similar scenarios, such as in the social clouds around MOOCs and news articles.

Modelling diversity is especially valuable in soft skills learning, where contextual awareness and understanding of different perspectives are crucial. This paper presents an application of a diversity analytics pipeline to generate domain diversity profiles for learners as captured in their comments while watching videos for learning a soft skill. The datasets for analysis were collected from a series of studies on learning presentation skills with Active Video Watching system (AVW-Space). Two user studies (with 37 postgraduates and 140 undergraduates, respectively) were compared. The learners’ diversity and personal profiles are used to further understand and highlight any notable patterns about their domain coverage on presentation skills.

Diversity has been the subject of study in various disciplines from biology to social science and computing. Respecting and utilising the diversity of the population is increasingly important to broadening knowledge. This paper describes a pipeline for diversity profiling of a pool of text in order to understand its coverage of an underpinning domain. The application is illustrated by using a domain ontology on presentation skills in a case study with 38 postgraduates who made comments while learning pitch presentations with the Active Video Watching system (AVW-Space). The outcome shows different patterns of coverage on the domain by the comments in each of the eight videos.

This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling’s framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners’ perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain.

The aim was to build an educational website to teach preschoolers aged 2-5 years old some skills; related to reading, writing and pronouncing alphabet, numbers, animal names, shapes and other common words. Also, assist parents and teachers by providing digital game-based learning environment to “edutainment” the children. The study illustrated the usability and usefulness of this educational game-based website and provided future insights for researchers and developers to take into account in future work.