NDLI: Emotion recognition in the wild from videos using images

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Emotion recognition in the wild from videos using images

Content Provider	ACM Digital Library
Author	Barsoum, Emad Zhang, Cha Bargal, Sarah Adel Ferrer, Cristian Canton
Abstract	This paper presents the implementation details of the proposed solution to the Emotion Recognition in the Wild 2016 Challenge, in the category of video-based emotion recognition. The proposed approach takes the video stream from the audio-video trimmed clips provided by the challenge as input and produces the emotion label corresponding to this video sequence. This output is encoded as one out of seven classes: the six basic emotions (Anger, Disgust, Fear, Happiness, Sad, Surprise) and Neutral. Overall, the system consists of several pipelined modules: face detection, image pre-processing, deep feature extraction, feature encoding and, finally, an SVM classification. This system achieves 59.42% validation accuracy, surpassing the competition baseline of 38.81%. With regard to test data, our system achieves 56.66% recognition rate, also improving the competition baseline of 40.47%. .
Starting Page	433
Ending Page	436
Page Count	4
File Format	PDF
ISBN	9781450345569
DOI	10.1145/2993148.2997627
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-10-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Support vector machines Emotion recognition Basic emotions Emotiw 2016 challenge Classification Deep features Convolutional neural networks
Content Type	Text
Resource Type	Article

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