다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Over the past few years, there has been an increased interest in automatic facial behavior analysis and understanding. We present OpenFace 2.0 - a tool intended for computer vision and machine learning researchers, affective computing community and people interested in building interactive applications based on facial behavior analysis. OpenFace 2.0 is an extension of OpenFace toolkit and is capable of more accurate facial landmark detection, head pose estimation, facial action unit recognition, and eye-gaze estimation. The computer vision algorithms which represent the core of OpenFace 2.0 demonstrate state-of-the-art results in all of the above mentioned tasks. Furthermore, our tool is capable of real-time performance and is able to run from a simple webcam without any specialist hardware. Finally, unlike a lot of modern approaches or toolkits, OpenFace 2.0 source code for training models and running them is freely available for research purposes.

https://par.nsf.gov/servlets/purl/10099458

OpenFace 2.0: Facial Behavior Analysis Toolkit

A new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions is presented in this paper. This database is unique from three aspects: i) the facial expressions are composed of judiciously selected subset of Action Units as well as the six basic emotions, and many actors/actresses are incorporated to obtain more realistic expression data; ii) a rich set of head pose variations are available; and iii) different types of face occlusions are included. Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis.

Bosphorus Database for 3D Face Analysis

An apposite and eminently readable reference for all behavioral science research and development

Encyclopedia of Statistics in Behavioral Science

Automatic affect analysis has attracted great interest in various contexts including the recognition of action units and basic or non-basic emotions In spite of major efforts, there are several open questions on what the important cues to interpret facial expressions are and how to encode them In this paper, we review the progress across a range of affect recognition applications to shed light on these fundamental questions We analyse the state-of-the-art solutions by decomposing their pipelines into fundamental components, namely face registration, representation, dimensionality reduction and recognition We discuss the role of these components and highlight the models and new trends that are followed in their design Moreover, we provide a comprehensive analysis of facial representations by uncovering their advantages and limitations; we elaborate on the type of information they encode and discuss how they deal with the key challenges of illumination variations, registration errors, head-pose variations, occlusions, and identity bias This survey allows us to identify open issues and to define future directions for designing real-world affect recognition systems

Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition

The various image-processing stages in a digital camera pipeline leave telltale footprints, which can be exploited as forensic signatures. These footprints consist of pixel defects, of unevenness of the responses in the charge-coupled device sensor, black current noise, and may originate from proprietary interpolation algorithms involved in color filter array. Various imaging device (camera, scanner, etc.) identification methods are based on the analysis of these artifacts. In this paper, we set to explore three sets of forensic features, namely binary similarity measures, image-quality measures, and higher order wavelet statistics in conjunction with SVM classifiers to identify the originating camera. We demonstrate that our camera model identification algorithm achieves more accurate identification, and that it can be made robust to a host of image manipulations. The algorithm has the potential to discriminate camera units within the same model.

Blind Identification of Source Cell-Phone Model

Face landmarking, defined as the detection and localization of certain characteristic points on the face, is an important intermediary step for many subsequent face processing operations that range from biometric recognition to the understanding of mental states. Despite its conceptual simplicity, this computer vision problem has proven extremely challenging due to inherent face variability as well as the multitude of confounding factors such as pose, expression, illumination and occlusions. The purpose of this survey is to give an overview of landmarking algorithms and their progress over the last decade, categorize them and show comparative performance statistics of the state of the art. We discuss the main trends and indicate current shortcomings with the expectation that this survey will provide further impetus for the much needed high-performance, real-life face landmarking operating at video rates.

/pdf/a-comparative-study-of-face-landmarking-techniques-3a6rp0iin0.pdf

A comparative study of face landmarking techniques

Engagement is crucial to designing intelligent systems that can adapt to the characteristics of their users. This paper focuses on the automatic analysis and classification of engagement based on humans’ and robot’s personality profiles in a triadic human–human–robot interaction setting. More explicitly, we present a study that involves two participants interacting with a humanoid robot, and investigate how participants’ personalities can be used together with the robot’s personality to predict the engagement state of each participant. The fully automatic system is first trained to predict the Big Five personality traits of each participant by extracting individual and interpersonal features from their nonverbal behavioural cues. Second, the output of the personality prediction system is used as an input to the engagement classification system. Third, we focus on the concept of “group engagement”, which we define as the collective engagement of the participants with the robot, and analyze the impact of similar and dissimilar personalities on the engagement classification. Our experimental results show that: 1) using the automatically predicted personality labels for engagement classification yields an F-measure on par with using the manually annotated personality labels, demonstrating the effectiveness of the automatic personality prediction module proposed; 2) using the individual and interpersonal features without utilizing personality information is not sufficient for engagement classification, instead incorporating the participants and robots personalities with individual/interpersonal features increases engagement classification performance; and 3) the best classification performance is achieved when the participants and the robot are extroverted, while the worst results are obtained when all are introverted.

Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions

In this paper we introduce a novel dataset, the Multimodal Human-Human-Robot-Interactions (MHHRI) dataset, with the aim of studying personality simultaneously in human-human interactions (HHI) and human-robot interactions (HRI) and its relationship with engagement. Multimodal data was collected during a controlled interaction study where dyadic interactions between two human participants and triadic interactions between two human participants and a robot took place with interactants asking a set of personal questions to each other. Interactions were recorded using two static and two dynamic cameras as well as two biosensors, and meta-data was collected by having participants fill in two types of questionnaires, for assessing their own personality traits and their perceived engagement with their partners (self labels) and for assessing personality traits of the other participants partaking in the study (acquaintance labels). As a proof of concept, we present baseline results for personality and engagement classification. Our results show that (i) trends in personality classification performance remain the same with respect to the self and the acquaintance labels across the HHI and HRI settings; (ii) for extroversion, the acquaintance labels yield better results as compared to the self labels; (iii) in general, multi-modality yields better performance for the classification of personality traits.

/pdf/multimodal-human-human-robot-interactions-mhhri-dataset-for-2f4u6ga0ge.pdf

Oya Celiktutan

Papers

Bosphorus Database for 3D Face Analysis

Blind Identification of Source Cell-Phone Model

A comparative study of face landmarking techniques

Fully Automatic Analysis of Engagement and Its Relationship to Personality in Human-Robot Interactions

Multimodal Human-Human-Robot Interactions (MHHRI) Dataset for Studying Personality and Engagement