Emotion Recognition Puns

tl;dr

In this explainer, we will be looking at emotion recognition technology and how it works as well as the issues related to its usage by different authorities. We will also be looking at the rights which are involved in this scenario as well as any laws in place to regulate its use and misuse.

What is emotion recognition technology?

In the short-lived but extremely engaging television series, “Lie to me”, the protagonist is the world's leading deception expert who studies facial expressions and involuntary body language to identify when a person is lying. He does this by studying “micro-expressions”, which are said to be involuntary facial expressions that last on a person’s face for a short time that give away their actual feelings before the person masks these feelings in an attempt to deceive or lie.

The protagonist of this show was based on and inspired by the real life work of American psychologist Paul Ekman, who is renowned for having come up with a theory of universal emotions which holds that, “(o)f all the human emotions we experience, there are seven universal emotions that we all feel, transcending language, regional, cultural, and ethnic differences”. This theory identifies seven universal facial expressions for these emotions, which are anger, disgust, fear, surprise, happiness, sadness and contempt, and is the basis for most of the recent development in artificial intelligence (AI) based emotion recognition technology.

Emotion recognition technology uses AI to identify and categorise emotions into these seven universal emotions or a combination thereof based on facial expressions it perceives from the subject and is used in conjunction with facial recognition technology. We recently came across this technology when the Lucknow Police announced its intention to use emotion recognition technology to track expressions of “distress” on the faces of women who come under the gaze of the AI enabled cameras in public places. The cameras would then automatically alert the nearest police station even before the woman in question takes any action to report any issue herself. Another troubling instance of the use of this technology was when a [Chinese subsidiary of Japanese camera maker Canon, Canon Information Technology, last year unveiled a new workspace

Hello!

I’m part of a team at the University of Aberdeen conducting an online study as part of my postgraduate research project looking at emotion recognition in adults with ADHD. We're using a dynamic morphing task to investigate this.

In addition, we're exploring the role of attention/working memory in emotion recognition – we want to know if performing another memory task at the same time as we're trying to recognise emotions affects how quickly and accurately we do this.

The experiment takes roughly 30-40 minutes to complete, and includes two small questionnaires.

We are looking for people aged 18-45 with and without ADHD to take part. If you're interested, please follow this link to participate: https://tstbl.co/211-383

Please note: Requires a computer running Google Chrome. Tablets/mobile devices are not supported.

If you know anyone else who might be interested, please feel free to crosspost this or share this link with others!

If you have any questions regarding the study, please feel free to comment/DM me, or contact t24ah20@abdn.ac.uk.

Thanks!

PEC/4710/2021/4

I am trying to recreate the model from this paper.

The task is to predict valence and arousal from a raw audio signal.

Our database is made of .mp3 files annotated on windows of 500ms.

The "feature extraction" is done by the multi-view CNN, fed then to a Bidirectional LSTM. The output should be a pair of values (Valence and Arousal).

I'm wondering on how the two sub-nets should be connected together in order to exploit the capability of LSTM to maintain temporal information.

Right now we have as input one excerpt of 500ms (22050 samples at 44.1kHz) with 1 channel.

Here's a sketch of our structure so far.

https://preview.redd.it/ex4gl3riph071.png?width=1131&format=png&auto=webp&s=d34ce26904f0be800f244b0f0bcabb8f857122e6

I feel like we should have another dimension, for example adding a timedistributed layer and flattening the output of the CNN, so that the input to the LSTM would be something like

(batch_size, timesteps, features)

with timestep dimension given by TimeDistributed layer and features obtained from flattening or max pooling the number of features, but I fear I am missing something.

Thanks in advance.

Hello, everyone! I am a psychology student and I am currently conducting a cross-cultural study (Western Europe - Russia) on emotion recognition. I would be very grateful if you would participate in my research. The survey is completely anonymous and will take no more than 30 minutes. You can take the survey in English or Russian (select the language in the upper right corner). Thank you very much in advance! Follow the link to complete the survey and find out all the additional information

https://arxiv.org/abs/2105.00173

https://t.co/hA50cf6m5C?amp=1

Hello, I have a small comprehension problem and need your help.

I am currently working on a method for emotion recognition in brain waves using an EEG.

The input parameters are the raw EEG data and the output should be arousal and valence values between -1 to 1.

My steps so far:

• Data cleaning -> remove artifacts and filtering the data.
• Feature extraction.

The Features I used:

(Mean, Standard deviations, Means of the absolute values of the first differences, Means of the absolute values of the second differences, Skewness, Kurtosis, Variance, Peak-to-peak (PTP) amplitude, Integral approximation of the spectrum (Theta, LowerAlpha, UpperAlpha, Beta, Gamma), Linelength, Hjorth Activity, Hjorth Mobility, Hjorth Complexity, Petrosian fractal dimension, Hurst fractal dimension)

And the Output of the Feature extraction look like this

Each row represents a participant trial and in each column represents a feature divided into channels.

The channels are in the typical 10-20 system. (Fp1, Fp2, Fz ...)

So the Feature table roughly look like this:

Since this is my own data collected through a study, it is unfortunately not labeled, so it has to be clustered using an unsupervised clustering algorithm.

What would be the next steps ? Can someone help me with this ?

I study computer science, so I program all the algorithms myself in Python. But I don't know how to transform the output data to fit into a clustering algorithm to get the valence and arousal values afterwards

I am curious, mine can get moderate emotion from voice, like "I don't understand" "I fixed it" l He make a (often imitating melody of words) whistle if he get it.

Hi Everyone! I am a BSc psychology student at the University of York and I would really appreciate your participation in a study for my dissertation.

I am investigating whether impaired emotion recognition commonly associated with Autism can be better explained by Alexithymia instead. This study can be completed by anyone aged 18 or above that does not have a diagnosis of Autism Spectrum Disorder. The experiment involves a face identity and emotion recognition task followed by two questionnaires. There is also a depression and anxiety questionnaire which is entirely optional. As usual, participation is completely anonymous and again, very greatly appreciated!

The implications of this research could help establish paradigms that would reduce misdiagnosis of ASD!

https://research.sc/participant/login/dynamic/22444227-BD01-45BB-9EE3-CE0A6B1D04F9

Hello, everyone! I am a psychology student and I am currently conducting a cross-cultural study (Western Europe - Russia) on emotion recognition. I would be very grateful if you would participate in my research. The survey is completely anonymous and will take no more than 30 minutes. You can take the survey in English or Russian (select the language in the upper right corner). Thank you very much in advance! Follow the link to complete the survey and find out all the additional information