SPECIAL SESSION in ICIP 2014

Within the IEEE International Conference on Image Processing, ICIP 2014, CNR and UCLAN, in cooperation with Bilkent University in Turkey, organized the Special Session on “Advances in Facial Morpho-Functional Sign Recognition and Analysis”,  Paris October 28, 2014.

Title: The Thermal Dimension of Psychophysiological and Emotional Responses Revealed by Thermal Infrared Imaging

Authors: D. Cardone, A. Merla
Abstract: Interpersonal communication and relationships rely on the continuous assessment of the psychophysiological state of the interlocutor. The thermal infrared imaging has been demonstrated to be a reliable tool for the non-invasive contact-less evaluation of vital signs, psychophysiological responses and emotional states. The use of this technique is quickly spreading in many fields, from social and developmental psychology to the psychometrics; or from the continuous monitoring of vital signs and stress up to the human-machine interaction. The state of the art of thermal infrared imaging in psychophysiology and in the assessment of emotional states is discussed to provide insights about its potentialities and limits, with special emphasis to the still open issues for image processing and real-time data analysis.
Title: Automatic Heart Rate Estimation from Painful Faces
Authors: P. Werner, A. Al-Hamadi, S. Walter, S. Gruss, H.C. Traue
Abstract: Non-contact measurement of the heart rate is more comfortable than classical methods and can facilitate new applications. However, current approaches are very susceptible to motion. Aiming at overcoming this limitation, we propose a new, more robust approach to estimate the heart rate from a videotaped face. It features non-planar motion compensation, fusion of multiple ROI signals, and a RANSAC-like time-domain heart rate estimation algorithm. In experiments with a comprehensive pain recognition dataset we show that our approach outperforms previous methods in the presence of spontaneous head movement and facial expression.

Title: Automatic Heart Rate Estimation from Painful Faces
Authors: P. Werner, A. Al-Hamadi, S. Walter, S. Gruss, H.C. Traue
Abstract: Non-contact measurement of the heart rate is more comfortable than classical methods and can facilitate new applications. However, current approaches are very susceptible to motion. Aiming at overcoming this limitation, we propose a new, more robust approach to estimate the heart rate from a videotaped face. It features non-planar motion compensation, fusion of multiple ROI signals, and a RANSAC-like time-domain heart rate estimation algorithm. In experiments with a comprehensive pain recognition dataset we show that our approach outperforms previous methods in the presence of spontaneous head movement and facial expression.

Title: Click and Share: a Face Recognition Tool for the Mobile Community
Authors: S. Casti, F. Sorrentino, L.D. Spano, R. Scateni
Abstract: In this paper, we describe an Android based application for mobile devices that allows users to quickly and easily identify faces in pictures, recognizing persons, and, thus, sharing pictures with them. Each identified person matches against a contact registered in the phone directory, and, if no match is found, the detected face can be used for the creation of a new contact. We discuss how face recognition in a mobile setting increases the efficiency of the users while sharing content created with the mobile device, automatically suggesting the people identified in a photo or a video. We show the effectiveness of the approach through a user test on a photo sharing task, showing that it reduces the need for tedious, in particular on mobile devices, user input (e.g., compared to Facebook). By this means, we envision an increase of the quality of the user experience when interacting with the components of his/her social network.

Title: Head Pose Tracking for Immersive Applications
Authors: P. Henriquez, O. Higuera, B. J. Matuszewski
Abstract: The paper describes a 3D head pose tracking system designed for immersive applications. The proposed system uses a range sensor to estimate head position and orientation in real-time. The proposed system is based on a random forest’s head detection and pose regression model. The main novel contributions include integration of the Kalman filter with the random forest, automatic detection of outliers and handling of the missing data. The envisaged applications of the proposed system include data normalization and data acquisition from remote locations. The latter is described in detail by integrating 3 degrees of freedom robotic camera head with the proposed head tracker. In this instance the proposed non-contact head tracking system is used to control the robotic camera by replicating measured operator’s head motion to improve his/her spatial awareness and sense of immersion, with the captured video shown on a head mounted display. The experimental section includes accuracy and robustness analysis. The system’s robustness is examined with respect to the head pose outliers and the missing head detections. The effects of the presence of the head mounted displays on the performance of the system are also assessed. Additionally a brief discussion of operators’ psycho-physical perception test is also included.