Emotional content is a crucial ingredient in user-generated videos. However, the sparsely expressed emotions in the user-generated video cause difficulties to emotions analysis in videos. In this paper, we propose a new neural approach---Bi-stream Emotion Attribution-Classification Network (BEAC-Net) to solve three related emotion analysis tasks: emotion recognition, emotion attribution and emotion-oriented summarization, in an integrated framework. BEAC-Net has two major constituents, an attribution network and a classification network. The attribution network extracts the main emotional segment that classification should focus on in order to mitigate the sparsity problem. The classification network utilizes both the extracted segment and the original video in a bi-stream architecture. We contribute a new dataset for the emotion attribution task with human-annotated ground-truth labels for emotion segments. Experiments on two video datasets demonstrate superior performance of the proposed framework and the complementary nature of the dual classification streams.
The emotional content in videos can be recognized from visual features, audio features and their combination. A number of work attempted to recognize emotionsand affects from speech @cite_29 @cite_47 @cite_13 . @cite_26 jointly uses speech and facial expressions. @cite_12 extracts mid-level audio-visual features. @cite_30 employs the visual, auditory, and textual modalities for video retrieval. @cite_8 provides a comprehensive technique that exploit audio, facial expressions, spatial-temporal information and mouth movements. Sparse coding @cite_48 @cite_61 also prove to be effective for emotion recognition. For a recent survey, we refer interested readers to @cite_43 .
@cite_30 @cite_61 @cite_26 @cite_8 @cite_48 @cite_29 @cite_43 @cite_47 @cite_13 @cite_12
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User-generated video collections are expanding rapidly in recent years, and systems for automatic analysis of these collections are in high demands. While extensive research efforts have been devoted to recognizing semantics like "birthday party" and "skiing", little attempts have been made to understand the emotions carried by the videos, e.g., "joy" and "sadness". In this paper, we propose a comprehensive computational framework for predicting emotions in user-generated videos. We first introduce a rigorously designed dataset collected from popular video-sharing websites with manual annotations, which can serve as a valuable benchmark for future research. A large set of features are extracted from this dataset, ranging from popular low-level visual descriptors, audio features, to high-level semantic attributes. Results of a comprehensive set of experiments indicate that combining multiple types of features--such as the joint use of the audio and visual clues--is important, and attribute features such as those containing sentiment-level semantics are very effective. Recognition of emotions in user-generated videos has attracted increasing research attention. Most existing approaches are based on spatial features extracted from video frames. However, due to the broad affective gap between spatial features of images and high-level emotions, the performance of existing approaches is restricted. To bridge the affective gap, we propose recognizing emotions in user-generated videos with kernelized features. We reformulate the equation of the discrete Fourier transform as a linear kernel function and construct a polynomial kernel function based on the linear kernel. The polynomial kernel is applied to spatial features of video frames to generate kernelized features. Compared with spatial features, kernelized features show superior discriminative capability. Moreover, we are the first to apply the sparse representation method to reduce the impact of noise contained in videos; this method helps contribute to performance improvement. Extensive experiments are conducted on two challenging benchmark datasets, that is, VideoEmotion-8 and Ekman-6. The experimental results demonstrate that the proposed method achieves state-of-the-art performance. In this paper we present the techniques used for the University of Montreal's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58 . Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03 on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56 . Emotion recognition grows to an important factor in future media retrieval and man machine interfaces. However, even human deciders often experience problems realizing one's emotion, especially of strangers. In this work we strive to recognize emotion independent of the person concentrating on the speech channel. Single feature relevance of acoustic features is a critical point, which we address by filter-based gain ratio calculation starting at a basis of 276 features. As optimization of a minimum set as a whole in general saves more extraction effort, we furthermore apply an SVM-SFFS wrapper based search. For a more robust estimation we also integrate spoken content information by a Bayesian net analysis of ASR outputs. Overall classification is realized in an early feature fusion by stacked ensembles of diverse base classifiers. Tests ran on a 3,947 movie and automotive interaction dialog-turns database consisting of 35 speakers. Remarkable overall performance can be reported in the discrimination of the seven discrete emotions named in the MPEG-4 standard with added neutrality