Abhinav Dhall

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Creating large, richly annotated databases depicting real-world or simulated real-world conditions is a challenging task. There has been a long understood need for recognition of human facial expressions in realistic video scenarios. Although many expression databases are available, research has been restrained by their limited scope due to their ‘lab(More)
The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0(More)
Quality data recorded in varied realistic environments is vital for effective human face related research. Currently available datasets for human facial expression analysis have been generated in highly controlled lab environments. We present a new static facial expression database Static Facial Expressions in the Wild (SFEW) extracted from a temporal(More)
The Second Emotion Recognition In The Wild Challenge (EmotiW) 2014 consists of an audio-video based emotion classification challenge, which mimics the real-world conditions. Traditionally, emotion recognition has been performed on data captured in constrained lab-controlled like environment. While this data was a good starting point, such lab controlled(More)
We propose a method for automatic emotion recognition as part of the FERA 2011 competition [1] . The system extracts pyramid of histogram of gradients (PHOG) and local phase quantisation (LPQ) features for encoding the shape and appearance information. For selecting the key frames, kmeans clustering is applied to the normalised shape vectors derived from(More)
The fourth Emotion Recognition in the Wild (EmotiW) challenge is a grand challenge in the ACM International Conference on Multimodal Interaction 2016, Tokyo. EmotiW is a series of benchmarking and competition effort for researchers working in the area of automatic emotion recognition in the wild. The fourth EmotiW has two sub-challenges: Video based emotion(More)
Automatic pain recognition from videos is a vital clinical application and, owing to its spontaneous nature, poses interesting challenges to automatic facial expression recognition (AFER) research. Previous pain vs no-pain systems have highlighted two major challenges: (1) ground truth is provided for the sequence, but the presence or absence of the target(More)
The recent advancement of social media has given users a platform to socially engage and interact with a larger population. Millions of images and videos are being uploaded everyday by users on the web from different events and social gatherings. There is an increasing interest in designing systems capable of understanding human manifestations of emotional(More)
This paper discusses the baseline for the Emotion Recognition in the Wild (EmotiW) 2016 challenge. Continuing on the theme of automatic affect recognition `in the wild', the EmotiW challenge 2016 consists of two sub-challenges: an audio-video based emotion and a new group-based emotion recognition sub-challenges. The audio-video based sub-challenge is(More)
Emotion recognition is a very active field of research. The Emotion Recognition In The Wild Challenge and Workshop (EmotiW) 2013 Grand Challenge consists of an audio-video based emotion classification challenges, which mimics real-world conditions. Traditionally, emotion recognition has been performed on laboratory controlled data. While undoubtedly(More)