Is it possible to predict the affect of a user just by observing her behavioral interaction through a video? How can we, for instance, predict a user's arousal in games by merely looking at the screen during play? In this paper we address these questions by employing three dissimilar deep convolutional neural network architectures in our attempt to learn the underlying mapping between video streams of gameplay and the player's arousal. We test the algorithms in an annotated dataset of 50 gameplay videos of a survival shooter game and evaluate the deep learned models' capacity to classify high vs low arousal levels. Our key findings with the demanding leave-one-video-out validation method reveal accuracies of over 78 on average and 98 at best. While this study focuses on games and player experience as a test domain, the findings and methodology are directly relevant to any affective computing area, introducing a general and user-agnostic approach for modeling affect.
Videos have been at the core of interest for both eliciting and modeling emotions in affective computing @cite_29 . Typically, the video features a human face (or a group of faces) and emotion is modelled through the detection of facial cues (see @cite_43 @cite_0 @cite_25 among many) due to theoretical frameworks and evidence supporting that facial expressions can convey emotion @cite_34 @cite_24 @cite_28 . Beyond the facial expression of a subject, aspects such as the body posture @cite_4 @cite_26 , gestures @cite_42 or gait @cite_27 @cite_18 , have been used as input for modeling affect.
@cite_18 @cite_26 @cite_4 @cite_28 @cite_29 @cite_42 @cite_0 @cite_43 @cite_24 @cite_27 @cite_34 @cite_25
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Automatic emotion recognition is of great value in many applications, however, to fully display the application value of emotion recognition, more portable, non-intrusive, inexpensive technologies need to be developed. Human gaits could reflect the walker’s emotional state, and could be an information source for emotion recognition. This paper proposed a novel method to recognize emotional state through human gaits by using Microsoft Kinect, a low-cost, portable, camera-based sensor. Fifty-nine participants’ gaits under neutral state, induced anger and induced happiness were recorded by two Kinect cameras, and the original data were processed through joint selection, coordinate system transformation, sliding window gauss filtering, differential operation, and data segmentation. Features of gait patterns were extracted from 3-dimentional coordinates of 14 main body joints by Fourier transformation and Principal Component Analysis (PCA). The classifiers NaiveBayes, RandomForests, LibSVM and SMO (Sequential Minimal Optimization) were trained and evaluated, and the accuracy of recognizing anger and happiness from neutral state achieved 80.5 and 75.4 . Although the results of distinguishing angry and happiness states were not ideal in current study, it showed the feasibility of automatically recognizing emotional states from gaits, with the characteristics meeting the application requirements. The conveyance and recognition of affect and emotion partially determine how people interact with others and how they carry out and perform in their day-to-day activities. Hence, it is becoming necessary to endow technology with the ability to recognize users' affective states to increase the technologies' effectiveness. This paper makes three contributions to this research area. First, we demonstrate recognition models that automatically recognize affective states and affective dimensions from non-acted body postures instead of acted postures. The scenario selected for the training and testing of the automatic recognition models is a body-movement-based video game. Second, when attributing affective labels and dimension levels to the postures represented as faceless avatars, the level of agreement for observers was above chance level. Finally, with the use of the labels and affective dimension levels assigned by the observers as ground truth and the observers' level of agreement as base rate, automatic recognition models grounded on low-level posture descriptions were built and tested for their ability to generalize to new observers and postures using random repeated subsampling validation. The automatic recognition models achieve recognition percentages comparable to the human base rates as hypothesized. The recognition of affective human communication may be used to provide developers with a rich source of information for creating systems that are capable of interacting well with humans. Posture has been acknowledged as an important modality of affective communication in many fields. Behavioral studies have shown that posture can communicate discrete emotion categories as well as affective dimensions. In the affective computing field, while models for the automatic recognition of discrete emotion categories from posture have been proposed, to our knowledge, there are no models for the automatic recognition of affective dimensions from static posture. As a continuation of our previous study, the two main goals of this study are: i) to build automatic recognition models to discriminate between levels of affective dimensions based on low-level postural features; and ii) to investigate both the discriminative power and the limitations of the postural features proposed. The models were built on the basis of human observers' ratings of posture according to affective dimensions directly (instead of emotion category) in conjunction with our posture features. Abstract Emotions are viewed as having evolved through their adaptive value in dealing with fundamental life-tasks. Each emotion has unique features: signal, physiology, and antecedent events. Each emotion also has characteristics in common with other emotions: rapid onset, short duration, unbidden occurrence, automatic appraisal, and coherence among responses. These shared and unique characteristics are the product of our evolution, and distinguish emotions from other affective phenomena. This paper illustrates our recent work on the analysis of expressive gesture related to the motion of the upper body (the head and the hands) in the context of emotional portrayals performed by professional actors. An experiment is presented which is the result of a multidisciplinary joint work. The experiment aims at (i) developing models and algorithms for analysis of such expressive content (ii) individuating which motion cues are involved in conveying the actorpsilas expressive intentions to portray four emotions (anger, joy, relief, sadness) via a scenario approach. The paper discusses the experiment in detail with reference to related conceptual issues, developed techniques, and the obtained results. Spontaneous facial expressions differ from posed expressions in both which muscles are moved, and in the dynamics of the movement. Advances in the field of automatic facial expression measurement will require development and assessment on spontaneous behavior. Here we present preliminary results on a task of facial action detection in spontaneous facial expressions. We employ a user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS). The system automatically detects frontal faces in the video stream and coded each frame with respect to 20 Action units. The approach applies machine learning methods such as support vector machines and AdaBoost, to texture-based image representations. The output margin for the learned classifiers predicts action unit intensity. Frame-by-frame intensity measurements will enable investigations into facial expression dynamics which were previously intractable by human coding. We present the Computer Expression Recognition Toolbox (CERT), a software tool for fully automatic real-time facial expression recognition, and officially release it for free academic use. CERT can automatically code the intensity of 19 different facial actions from the Facial Action Unit Coding System (FACS) and 6 different protoypical facial expressions. It also estimates the locations of 10 facial features as well as the 3-D orientation (yaw, pitch, roll) of the head. On a database of posed facial expressions, Extended Cohn-Kanade (CK+ [1]), CERT achieves an average recognition performance (probability of correctness on a two-alternative forced choice (2AFC) task between one positive and one negative example) of 90.1 when analyzing facial actions. On a spontaneous facial expression dataset, CERT achieves an accuracy of nearly 80 . In a standard dual core laptop, CERT can process 320 × 240 video images in real time at approximately 10 frames per second. The present study examined the potential for information provided in a person's style of walking to reveal certain emotions. Ten subjects observed five walkers expressing four different emotions and made emotion identifications as well as judgments about specific gait characteristics. Results revealed that subjects were able to identify sadness, anger, happiness, and pride from gait information at better than chance levels; however, identifications of pride were significantly less accurate than were identifications of sadness and anger. In addition, subjects' acuracy varied across the five walkers. Results also revealed that gait characteristics such as the amount of arm swing, stride length, heavyfootedness, and walking speed differentiated the emotions expressed by walkers. Most studies investigating the recognition of facial expressions have focused on static displays of intense expressions. Consequently, researchers may have underestimated the importance of motion in deciphering the subtle expressions that permeate real-life situations. In two experiments, we examined the effect of motion on perception of subtle facial expressions and tested the hypotheses that motion improves affect judgment by (a) providing denser sampling of expressions, (b) providing dynamic information, (c) facilitating configural processing, and (d) enhancing the perception of change. Participants viewed faces depicting subtle facial expressions in four modes (single-static, multi-static, dynamic, and first-last). Experiment 1 demonstrated a robust effect of motion and suggested that this effect was due to the dynamic property of the expression. Experiment 2 showed that the beneficial effect of motion may be due more specifically to its role in perception of change. Together, these experiments demons... We propose a technique for producing ‘visual explanations’ for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent. Our approach – Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say logits for ‘dog’ or even a caption), flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad- CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g. VGG), (2) CNNs used for structured outputs (e.g. captioning), (3) CNNs used in tasks with multi-modal inputs (e.g. visual question answering) or reinforcement learning, without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are more faithful to the underlying model, and (d) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show even non-attention based models can localize inputs. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a ‘stronger’ deep network from a ‘weaker’ one even when both make identical predictions. Our code is available at https: github.com ramprs grad-cam along with a demo on CloudCV [2] and video at youtu.be COjUB9Izk6E.