Two channels have been distinguished in human interaction: one transmits explicit messages, which may be about anything or nothing; the other transmits implicit messages about the speakers themselves. Both linguistics and technology have invested enormous efforts in understanding the first, explicit channel, but the second is not as well understood. Understanding the other party's emotions is one of the key tasks associated with the second, implicit channel. To tackle that task, signal processing and analysis techniques have to be developed, while, at the same time, consolidating psychological and linguistic analyses of emotion. This article examines basic issues in those areas. It is motivated by the PKYSTA project, in which we aim to develop a hybrid system capable of using information from faces and voices to recognize people's emotions.

The face and gesture recognition network (FG-NET) ageing database was released in 2004 in an attempt to support research activities aimed at understanding the changes in facial appearance caused by ageing. Since then the database was used for carrying out research in various disciplines including age estimation, age-invariant face recognition and age progression. On the basis of the analysis of published work where the FG-NET ageing database was used, conclusions related to the type of research carried out in relation to the impact of the dataset in shaping up the research topic of facial ageing are presented. This study also includes a review of key articles from different thematic areas, where the FG-NET ageing database was used and the presentation of benchmark results. The ultimate aims of this study are to present concrete facts related to research activities in facial ageing during the past decade, provide an indication of the main methodologies adopted, present a comprehensive list of benchmark results and most importantly provide roadmaps for future trends, requirements and research directions in facial ageing.

Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. However, manual image annotation, even for creating training sets for machine learning algorithms, requires hard effort and contains human judgment errors and subjectivity. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are pursued. In this work, we investigate whether tags accompanying photos in the Instagram can be considered as image annotation metadata. If such a claim is proved then Instagram could be used as a very rich, easy to collect automatically, source of training data for the development of AIA techniques. Our hypothesis is that Instagram hashtags, and especially those provided by the photo owner/creator, express more accurately the content of a photo compared to the tags assigned to a photo during explicit image annotation processes like crowdsourcing. In this context, we explore the descriptive power of hashtags by examining whether other users would use the same, with the owner, hashtags to annotate an image. For this purpose 1000 Instagram images were collected and one to four hashtags, considered as the most descriptive ones for the image in question, were chosen among the hashtags used by the photo owner. An online database was constructed to generate online questionnaires containing 20 images each, which were distributed to experiment participants so they can choose the best suitable hashtag for every image according to their interpretation. Results show that an average of 66% of the participants hashtag choices coincide with those suggested by the photo owners; thus, an initial evidence towards our hypothesis confirmation can be claimed

In the framework of MPEG-4, one can include applications where virtual agents, utilizing both textual and multisensory data, including facial expressions and nonverbal speech help systems become accustomed to the actual feelings of the user. Applications of this technology are expected in educational environments, virtual collaborative workplaces, communities, and interactive entertainment. Facial animation has gained much interest within the MPEG-4 framework; with implementation details being an open research area (Tekalp, 1999). In this paper, we describe a method for enriching human computer interaction, focusing on analysis and synthesis of primary and intermediate facial expressions (Ekman and Friesen (1978)). To achieve this goal, we utilize facial animation parameters (FAPs) to model primary expressions and describe a rule-based technique for handling intermediate ones. A relation between FAPs and the activation parameter proposed in classical psychological studies is established, leading to parameterized facial expression analysis and synthesis notions, compatible with the MPEG-4 standard.

The long-term performance of biometric authentication systems is highly depended on the permanence of biometric features stored in biometric templates. Aging variation causes modifications on biometric features that affect the matching between stored and captured biometric templates causing in that way deterioration in the performance of biometric authentication systems. In this study the authors attempt to quantify the effects of aging for different biometric modalities, so that it is possible to draw conclusions related to the effect of aging on different types of biometric templates. In this context variations between distributions containing biometric features from different age groups are quantified, allowing in that way the definition of age-sensitive and age-invariant biometric features. An important aspect of the proposed approach is the standardised and generic nature of the approach that allows the derivation of comparative results between different modalities and different feature vectors. The work presented in this study provides a valuable tool for selecting, either age-invariant features for use in identity authentication applications, or for selecting age-sensitive features for age-estimation-related applications.

The era of big data has, among others, three characteristics: the huge amounts of data created every day and in every form by everyday people, artificial intelligence tools to mine information from those data and effective algorithms that allow this data mining in real or close to real time. On the other hand, opinion mining in social media is nowadays an important parameter of social media marketing. Digital media giants such as Google and Facebook developed and employed their own tools for that purpose. These tools are based on publicly available software libraries and tools such as Word2Vec (or Doc2Vec) and fasttext, which emphasize topic modeling and extract low-level features using deep learning approaches. So far, researchers have focused their efforts on opinion mining and especially on sentiment analysis of tweets. This trend reflects the availability of the Twitter API that simplifies automatic data (tweet) collection and testing of the proposed algorithms in real situations. However, if we are really interested in realistic opinion mining we should consider mining opinions from social media platforms such as Facebook and Instagram, which are far more popular among everyday people. The basic purpose of this paper is to compare various kinds of low-level features, including those extracted through deep learning, as in fasttext and Doc2Vec, and keywords suggested by the crowd, called crowd lexicon herein, through a crowdsourcing platform. The application target is sentiment analysis of tweets and Facebook comments on commercial products. We also compare several machine learning methods for the creation of sentiment analysis models and conclude that, even in the era of big data, allowing people to annotate (a small portion of) data would allow effective artificial intelligence tools to be developed using the learning by example paradigm.