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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)
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)
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)
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)
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)
Depression is a severe mental health disorder with high societal costs. Current clinical practice depends almost exclusively on self-report and clinical opinion, risking a range of subjective biases. The long-term goal of our research is to develop assistive technologies to support clinicians and sufferers in the diagnosis and monitoring of treatment(More)
We study the problem of expression analysis for a group of people. Automatic facial expression analysis has seen much research in recent times. However, little attention has been given to the estimation of the overall expression theme conveyed by an image of a group of people. Specifically, this work focuses on formulating a framework for happiness(More)
Facial expression analysis has been a very active field of research in recent years. This paper proposes a method for finding the apex of an expression, e.g. happiness, in a video containing a group of people based on expression intensity estimation. The proposed method is directly applied to video summarisa-tion based on group happiness and timestamps;(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)