Affective video content analysis aims at the automatic recognition of emotions elicited by videos. It has a large number of applications, including mood based personalized content recommendation , video indexing , and efficient movie visualization and browsing . Beyond the analysis of existing video material, affective computing techniques can also be used to generate new content, e.g., movie summarization , personalized soundtrack recommendation to make user-generated videos more attractive . Affective techniques can furthermore be used to enhance the user engagement with advertising content by optimizing the way ads are inserted inside videos .
While major progress has been achieved in computer vision for visual object detection, high-level concept recognition, and scene understanding, a natural further step is the modeling and recognition of affective concepts. This has recently received increasing interest from research communities, e.g., computer vision and machine learning, with an overall goal of endowing computers with human-like perception capabilities.
Efficient training and benchmarking of computational models, however, require a large and diverse collection of data annotated with ground truth, which is often difficult to collect, and particularly in the field of affective computing. To address this issue we created the LIRIS-ACCEDE dataset. In contrast to most existing datasets that contain few video resources and have limited accessibility due to copyright constraints, LIRIS-ACCEDE consists of videos with a large content diversity annotated along emotional dimensions. The annotations are made according to the expected emotion of a video, which is the emotion that the majority of the audience feels in response to the same content. All videos are shared under Creative Commons licenses and can thus be freely distributed without copyright issues. The dataset (the videos, annotations, features and protocols) are publicly available, and it is currently composed of a total of six collections.
Credits and license information: (a) Cloudland, LateNite Films, shared under CC BY 3.0 Unported license at http://vimeo.com/17105083, (b) Origami, ESMA MOVIES, shared under CC BY 3.0 Unported license at http://vimeo.com/52560308, (c) Payload, Stu Willis, shared under CC BY 3.0 Unported license at http://vimeo.com/50509389, (d) The room of Franz Kafka, Fred. L’Epee, shared under CC BY-NC-SA 3.0 Unported license at http://vimeo.com/14482569, (e) Spaceman, Jono Schaferkotter & Before North, shared under CC BY-NC 3.0 Unported License license at http://vodo.net/spaceman.
Dataset & Collections
The LIRIS-ACCEDE dataset is composed of movies and excerpts from movies under Creative Commons licenses that enable the dataset to be publicly shared. The set contains 160 professionally made and amateur movies, with different movie genres such as horror, comedy, drama, action and so on. Languages are mainly English, with a small set of Italian, Spanish, French and others subtitled in English. The set has been used to create the six collections that are part of the dataset. The two collections that were originally proposed are the Discrete LIRIS-ACCEDE collection, which contains short excerpts of movies, and the Continuous LIRIS-ACCEDE collection, which comprises long movies. Moreover, since 2015, the set has been used for tasks related to affect/emotion at the MediaEval Benchmarking Initiative for Multimedia Evaluation , where each year it was enriched with new data, features and annotations. Thus, the dataset also includes the four additional collections dedicated to these tasks.
The movies are available together with emotional annotations. When dealing with emotional video content analysis, the goal is to automatically recognize emotions elicited by videos. In this context, three types of emotions can be considered: intended, induced and expected emotions. The intended emotion is the emotion that the film maker wants to induce in the viewers. The induced emotion is the emotion that a viewer feels in response to the movie. The expected emotion is the emotion that the majority of the audience feels in response to the same content. While the induced emotion is subjective and context dependent, the expected emotion can be considered objective, as it reflects the more-or-less unanimous response of a general audience to a given stimulus. Thus, the LIRIS-ACCEDE dataset focuses on the expected emotion. The representation of emotions we are considering is the dimensional one, based on valence and arousal. Valence is defined on a continuous scale from most negative to most positive emotions, while arousal is defined continuously from calmest to most active emotions . Moreover, violence annotations were provided in the MediaEval 2015 Affective Impact of Movies collection, while fear annotations were provided in the MediaEval 2016 and 2017 Emotional Impact of Movies collections.
|Discrete LIRIS-ACCEDE collection
||A total of 160 films from various genres split into 9,800 short clips with valence and arousal annotations. More details below.
|Continuous LIRIS-ACCEDE collection
||A total of 30 films with valence and arousal annotations per second. More details below.
|MediaEval 2015 Affective Impact of Movies collection
||A subset of the films with labels for the presence of violence, as well as for the felt valence and arousal. More details below.
|MediaEval 2016 Emotional Impact of Movies collection
||A subset of the films with score annotations for the expected valence and arousal. More details below.
|MediaEval 2017 Emotional Impact of Movies collection
||A subset of the films with valence and arousal values and a label for the presence of fear for each 10 second segment, as well as precomputed features. More details below.
|MediaEval 2018 Emotional Impact of Movies collection
||A subset of the films with valence and arousal values for each second, begin-end times of scenes containing fear, as well as precomputed features. More details below.
The ground truth for the Discrete LIRIS-ACCEDE collection consists of the ranking of all video clips along both valence and arousal dimensions. These rankings were obtained thanks to a pairwise video clips comparison protocol that has been designed to be used through crowdsourcing (with CrowdFlower service). Thus, for each pair of video clips presented to raters, they had to select the one which conveyed most strongly the given emotion in terms of valence or arousal. The high inter-annotator agreement that was achieved reflects that annotations were fully consistent, despite the large diversity of our raters’ cultural backgrounds. Affective ratings (scores) were also collected for a subset of the 9,800 movies in order to cross-validate the crowdsourced annotations. The affective ratings also made learning of Gaussian Processes for Regression possible, to model the noisiness from measurements and map the whole ranked LIRIS-ACCEDE dataset into the 2D valence-arousal affective space. More details can be found in .
To collect the ground truth for the continuous and MediaEval 2016, 2017 and 2018 collections, which consisted of valence and arousal scores for every movie second, French annotators had to continuously indicate their level of valence and arousal while watching the movies using a modified version of the GTrace annotation tool  and a joystick. Each annotator continuously annotated one subset of the movies considering the induced valence, and another subset considering the induced arousal. Thus, each movie was continuously annotated by three to five different annotators. Then, the continuous valence and arousal annotations from the annotators were down-sampled by averaging the annotations over windows of 10 seconds with a shift of 1 second overlap (i.e., yielding 1 value per second) in order to remove any noise due to unintended movements of the joystick. Finally, the post-processed continuous annotations were averaged in order to create a continuous mean signal of the valence and arousal self-assessments, ranging from -1 (most negative for valence, most passive for arousal) to +1 (most positive for valence, most active for arousal). The details of this process are given in .
The ground truth for violence annotation, used in the MediaEval 2015 Affective Impact of Movies collection, was collected as follows. First, all the videos were annotated separately by two groups of annotators from two different countries. For each group, regular annotators labeled all the videos, which were then reviewed by master annotators. Regular annotators were graduate students (typically single with no children) and master annotators were senior researchers. Within each group, each video received 2 different annotations, which were then merged by the master annotators into the final annotation for the group. Finally, the achieved annotations from the two groups were merged and reviewed once more by the task organizers. The details can be found in .
The ground truth for fear annotations, used in the MediaEval 2017 and 2018 Emotional Impact of Movies collections, was generated using a tool specifically designed for the classification of audio-visual media allowing to perform annotation while watching the movie (at the same time). The annotations have been realized by two well-experienced team members of NICAM , both of them trained in classification of media. Each movie was annotated by one annotator reporting the start and stop times of each sequence in the movie expected to induce fear.
Through its six collections, the LIRIS-ACCEDE dataset constitutes a dataset of choice for affective video content analysis. It is one of the largest dataset for this purpose, and is regularly enriched with new data, features and annotations. In particular, it is used for the Emotional Impact of Movies tasks at MediaEval Benchmarking Initiative for Multimedia Evaluation. As all the movies are under Creative Commons licenses, the whole dataset can be freely shared and used by the research community, and is available at http://liris-accede.ec-lyon.fr.
Discrete LIRIS-ACCEDE collection 
In total 160 films and short films with different genres were used and were segmented into 9,800 video clips. The total time of all 160 films is 73 hours 41 minutes and 7 seconds, and a video clip was extracted on average every 27s. The 9,800 segmented video clips last between 8 and 12 seconds and are representative enough to conduct experiments. Indeed, the length of extracted segments is large enough to get consistent excerpts allowing the viewer to feel emotions, while being small enough to make the viewer feel only one emotion per excerpt.
The content of the movie was also considered to create homogeneous, consistent and meaningful excerpts that were not meant to disturb the viewers. A robust shot and fade in/out detection was implemented to make sure that each extracted video clip started and ended with a shot or a fade. Furthermore, the order of excerpts within a film was kept, allowing the study of temporal transitions of emotions.
Several movie genres are represented in this collection of movies, such as horror, comedy, drama, action, and so on. Languages are mainly English with a small set of Italian, Spanish, French and others subtitled in English. For this collection the 9,800 video clips are ranked according to valence, from the clip inducing the most negative emotion to the most positive, and to arousal, from the clip inducing the calmest emotion to the most active emotion. Besides the ranks, the emotional scores (valence and arousal) are also provided for each clip.
Continuous LIRIS-ACCEDE collection 
The movie clips for the Discrete collection were annotated globally, for which a single value of arousal and valence was used to represent a whole 8 to 12-second video clip. In order to allow deeper investigations into the temporal dependencies of emotions (since a felt emotion may influence the emotions felt in the future), longer movies were considered in this collection. To this end, a selection of 30 movies from the set of 160 was made such that their genre, content, language and duration were diverse enough to be representative of the original Discrete LIRIS-ACCEDE dataset. The selected videos are between 117 and 4,566 seconds long (mean = 884.2s ± 766.7s SD). The total length of the 30 selected movies is 7 hours, 22 minutes and 5 seconds. The emotional annotations consist of a score of expected valence and arousal for each second of each movie.
MediaEval 2015 Affective Impact of Movies collection 
This collection has been used as the development and test sets for the MediaEval 2015 Affective Impact of Movies Task. The overall use case scenario of the task was to design a video search system that used automatic tools to help users find videos that fitted their particular mood, age or preferences. To address this, two subtasks were proposed:
• Induced affect detection: the emotional impact of a video or movie can be a strong indicator for search or recommendation;
• Violence detection: detecting violent content is an important aspect of filtering video content based on age.
The 9,800 video clips from the Discrete LIRIS-ACCEDE section were used as development set, and an additional 1100 movie clips were proposed for the test set. For each of the 10,900 video clips, the annotations consist of: a binary value to indicate the presence of violence, the class of the excerpt for felt arousal (calm-neutral-active), and the class for felt valence (negative-neutral-positive).
MediaEval 2016 Emotional Impact of Movies collection 
The MediaEval 2016 Emotional Impact of Movies task required participants to deploy multimedia features to automatically predict the emotional impact of movies, in terms of valence and arousal. Two subtasks were proposed:
• Global emotion prediction: given a short video clip (around 10 seconds), participants’ systems were expected to predict a score of induced valence (negative-positive) and induced arousal (calm-excited) for the whole clip;
• Continuous emotion prediction: as an emotion felt during a scene may be influenced by the emotions felt during the previous scene(s), the purpose here was to consider longer videos, and to predict the valence and arousal continuously along the video. Thus, a score of induced valence and arousal were to be provided for each 1s-segment of each video.
The development set was composed of the Discrete LIRIS-ACCEDE part for the first subtask, and the Continuous LIRIS-ACCEDE part for the second subtask. In addition to the development set, a test set was also provided to assess participants’ methods performance. A total of 49 new movies under Creative Commons licenses were added. With the same protocol as the one used for the development set, 1,200 additional short video clips were extracted for the first subtask (between 8 and 12 seconds), while 10 long movies (from 25 minutes to 1 hour and 35 minutes) were selected for the second subtask (for a total duration of 11.48 hours). Thus, the annotations consist of a score of expected valence and arousal for each movie clip used for the first subtask, and a score of expected valence and arousal for each second of the movies for the second subtask.
MediaEval 2017 Emotional Impact of Movies collection 
This collection was used for the MediaEval 2017 Emotional Impact of Movies task. Here, only long movies were considered, and the emotion was considered in terms of valence, arousal and fear. The following two subtasks were proposed for which the emotional impact had to be predicted for consecutive 10-second segments, which slid over the whole movie with a shift of 5 seconds:
• Valence/Arousal prediction: participants’ systems were supposed to predict a score of expected valence and arousal for each consecutive 10-second segment;
• Fear prediction: the purpose here was to predict whether each consecutive 10-second segments was likely to induce fear or not. The targeted use case was the prediction of frightening scenes to help systems protecting children from potentially harmful video content. This subtask is complementary to the valence/arousal prediction task in the sense that the mapping of discrete emotions into the 2D valence/arousal space is often overlapped (for instance, fear, disgust and anger are overlapped since they are characterized with very negative valence and high arousal).
The Continuous LIRIS-ACCEDE collection was used as the development test for both subtasks. The test set consisted of a selection of new 14 new movies under Creative Commons licenses other than the selection of the 160 original movies. They are between 210 and 6,260 seconds long. The total length of the 14 selected movies is 7 hours, 57 minutes and 13 seconds. In addition to the video data, general purpose audio and visual content features were also provided, including Deep features, Fuzzy Color and Texture Histogram, Gabor features. The annotations consist of a valence value, an arousal value and a binary value for each 10-second segment to indicate if the segment was supposed to induce fear or not.
MediaEval 2018 Emotional Impact of Movies collection 
The MediaEval 2018 Emotional Impact of Movies task is similar to the one of 2017. However, in this case, more data was provided and a prediction of the emotional impact needed to be made for every second in movies rather than for 10-second segments as before. The two subtasks were:
• Valence and Arousal prediction: participants’ systems had to predict a score of expected valence and arousal continuously (every second) for each movie;
• Fear detection: the purpose here was to predict beginning and ending times of sequences inducing fear in movies. The targeted use case was the detection of frightening scenes to help systems protecting children from potentially harmful video content.
The development set for both subtasks consisted of the movies from the Continuous LIRIS-ACCEDE collection, as well as from the test set of the MediaEval 2017 Emotional Impact of Movies collection, i.e. 44 movies for a total duration of 15 hours and 20 minutes. The test set consisted of 12 other movies selected from the set of 160 movies, for a total duration of 8 hours and 56 minutes. Like the 2017 collection, in addition to the video data, general purpose audio and visual content features were also provided. The annotations consist of valence and arousal values for each second of the movies (for the first subtasks) as well as the beginning and ending times of each sequence in movies inducing fear (for the second subtask).
This work was supported in part by the French research agency ANR through the VideoSense Project under the Grant 2009 CORD 026 02 and through the Visen project within the ERA-NET CHIST-ERA framework under the grant ANR-12-CHRI-0002-04.
Should you have any inquiries or questions about the dataset, do not hesitate to contact us by email at: emmanuel dot dellandrea at ec-lyon dot fr.
 L. Canini, S. Benini, and R. Leonardi, “Affective recommendation of movies based on selected connotative features”, in IEEE Transactions on Circuits and Systems for Video Technology, 23(4), 636–647, 2013.
 S. Zhang, Q. Huang, S. Jiang, W. Gao, and Q. Tian. 2010, “Affective visualization and retrieval for music video”, in IEEE Transactions on Multimedia 12(6), 510–522, 2010.
 S.Zhao, H.Yao, X.Sun, X.Jiang, and P. Xu., “Flexible presentation of videos based on affective content analysis”, in Advances in Multimedia Modeling, 2013.
 H. Katti, K. Yadati, M. Kankanhalli, and C. Tat-Seng, “Affective video summarization and story board generation using pupillary dilation and eye gaze”, in IEEE International Symposium on Multimedia (ISM), 2011.
 R.R. Shah,Y. Yu, and R. Zimmermann, “Advisor: Personalized video soundtrack recommendation by late fusion with heuristic rankings”, in ACM International Conference on Multimedia, 2014.
 K. Yadati, H. Katti, and M. Kankanhalli, “Cavva: Computational affective video-in-video advertising”, in IEEE Transactions on Multimedia 16(1), 15–23, 2014.
 A. Hanjalic, “Extracting moods from pictures and sounds: Towards truly personalized TV”, in IEEE Signal Processing Magazine, 2006.
 J.A. Russell, “Core affect and the psychological construction of emotion”, in Psychological Review, 2003.
 Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, “LIRIS-ACCEDE: A Video Database for Affective Content Analysis,” in IEEE Transactions on Affective Computing, 2015.
 Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, “Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos,” in 2015 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2015.
 M. Sjöberg, Y. Baveye, H. Wang, V. L. Quang, B. Ionescu, E. Dellandréa, M. Schedl, C.-H. Demarty, and L. Chen, “The mediaeval 2015 affective impact of movies task,” in MediaEval 2015 Workshop, 2015.
 E. Dellandrea, L. Chen, Y. Baveye, M. Sjoberg and C. Chamaret, “The MediaEval 2016 Emotional Impact of Movies Task”, in Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, The Netherlands, October 20-21, 2016.
 E. Dellandrea, M. Huigsloot, L. Chen, Y. Baveye and M. Sjoberg, “The MediaEval 2017 Emotional Impact of Movies Task”, in Working Notes Proceedings of the MediaEval 2017 Workshop, Dublin, Ireland, September 13-15, 2017.
 E. Dellandréa, M. Huigsloot, L. Chen, Y. Baveye, Z. Xiao and M. Sjöberg, “The MediaEval 2018 Emotional Impact of Movies Task”, Working Notes Proceedings of the MediaEval 2018 Workshop, Sophia Antipolis, France, October 29-31, 2018.
 R. Cowie, M. Sawey, C. Doherty, J. Jaimovich, C. Fyans, and P. Stapleton, “Gtrace: General trace program compatible with emotionML”, in Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2013.