Mental health issues of breast cancer patients and survivors are detrimental to their recovery, reintegration to normal living, and quality of life. With the increasing popularity of social media platforms and online browsing, combined with the rise of Natural Language Processing (NLP), there exists great potential in analyzing textual information of various online user activities to infer important indicators about a person’s online behaviour and emotional well-being. This work proposes a NLP-based framework for emotion extraction from social media posts and for user interest identification from browser history data. We experimented with contextual representations by fine-tuning pre-trained models like BERT combined with a fully connected layer, stacked LSTM and Bi-LSTM layers, and external resources in the form of emotion lexicons and self-attention. We evaluated our models on curated datasets derived from the combination of open datasets and achieved state-of-the-art performance. Overall, our work provides evidence of the potential for non-obtrusive extraction of online behaviour indicators that reflect the emotional well-being of patients.

The advent of affordable real-time 3D sensors is expected to have a major impact in face and gesture recognition applications, not excluding facial expression recognition. In this paper we review our latest results, a completely automated facial action and facial expression recognition system based on a real-time 3D sensor and its evaluation in uncontrolled conditions. We also highlight existing challenges and propose future directions.

A novel surveillance system integrating 2D and 3D facial data is presented in this paper, based on a low-cost sensor capable of real-time acquisition of 3D images and associated color images of a scene. Depth data is used for robust face detection, localization and 3D pose estimation, as well as for compensating pose and illumination variations of facial images prior to classification . The proposed system was tested under an open-set identification scenario for surveillance of humans passing through a relatively constrained area. Experimental results demonstrate the accuracy and robustness of the system under a variety of conditions usually encountered in surveillance applications.

This paper presents a complete face authentication system integrating both two-dimensional (color or intensity) and three-dimensional (3-D) range data, based on a low-cost 3-D sensor, capable of real-time acquisition of 3-D and color images. Novel algorithms are proposed that exploit depth information to achieve robust face detection and localization under conditions of background clutter, occlusion, face pose alteration, and harsh illumination. The well-known embedded hidden Markov model technique for face authentication is applied to depth maps and color images. To cope with pose and illumination variations, the enrichment of face databases with synthetically generated views is proposed. The performance of the proposed authentication scheme is tested thoroughly on two distinct face databases of significant size. Experimental results demonstrate significant gains resulting from the combined use of depth and color or intensity information.

The paper presents several novel 3D image analysis algorithms, applied towards the segmentation and modeling of faces and hands. These are subsequently used to build a face-based authentication system and a system for human-computer interaction based on static and dynamic gestures. The system relies on an active stereo sensor that uses a structured light approach to obtain 3D information. In this paper we demonstrate how the use of 3D information may significantly improve the efficiency of traditional face and gesture recognition techniques that use 2D images only.