Different Ways To Say Poker Face

4/7/2022

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Synonyms for poker face include deadpan, deadpan expression, impassivity, inscrutability, mask, seriousness, straight face, impassiveness, serious expression and countenance. Find more similar words at wordhippo.com! Gaga went on explaining more about ‘Poker face’ to Fashionista101, she explained that the song is about her playing with guys as if she was a poker player. She said: “The song speaks in reference to the poker game, ‘Texas Hold Em.’ So I said, ‘I wanna hold him like they do in Texas, please.’.

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Our emotions influenceevery aspect of our lives, from our health and how we learn,to how we do business and make decisions, big ones and small. Our emotions also influencehow we connect with one another. We've evolved to livein a world like this, but instead, we're livingmore and more of our lives like this — this is the text messagefrom my daughter last night — in a world that's devoid of emotion. So I'm on a mission to change that. I want to bring emotionsback into our digital experiences.

I started on this path 15 years ago. I was a computer scientist in Egypt, and I had just gotten accepted toa Ph.D. program at Cambridge University. So I did something quite unusual for a young newlywed Muslim Egyptian wife: With the support of my husband,who had to stay in Egypt, I packed my bags and I moved to England. At Cambridge, thousands of milesaway from home, I realized I was spendingmore hours with my laptop than I did with any other human. Yet despite this intimacy, my laptophad absolutely no idea how I was feeling. It had no idea if I was happy, having a bad day, or stressed, confused, and so that got frustrating. Even worse, as I communicatedonline with my family back home, I felt that all my emotionsdisappeared in cyberspace. I was homesick, I was lonely,and on some days I was actually crying, but all I had to communicatethese emotions was this. (Laughter) Today's technologyhas lots of I.Q., but no E.Q.; lots of cognitive intelligence,but no emotional intelligence. So that got me thinking, what if our technologycould sense our emotions? What if our devices could sensehow we felt and reacted accordingly, just the way an emotionallyintelligent friend would? Those questions led me and my team to create technologies that can readand respond to our emotions, and our starting point was the human face.

So our human face happens to beone of the most powerful channels that we all use to communicatesocial and emotional states, everything from enjoyment, surprise, empathy and curiosity. In emotion science, we call eachfacial muscle movement an action unit. So for example, action unit 12, it's not a Hollywood blockbuster, it is actually a lip corner pull,which is the main component of a smile. Try it everybody. Let's getsome smiles going on. Another example is action unit 4.It's the brow furrow. It's when you draw your eyebrows together and you create allthese textures and wrinkles. We don't like them, but it'sa strong indicator of a negative emotion. So we have about 45 of these action units, and they combine to expresshundreds of emotions.

Teaching a computer to readthese facial emotions is hard, because these action units,they can be fast, they're subtle, and they combine in many different ways. So take, for example,the smile and the smirk. They look somewhat similar,but they mean very different things. (Laughter) So the smile is positive, a smirk is often negative. Sometimes a smirkcan make you become famous. But seriously, it's importantfor a computer to be able to tell the differencebetween the two expressions.

So how do we do that? We give our algorithms tens of thousands of examplesof people we know to be smiling, from different ethnicities, ages, genders, and we do the same for smirks. And then, using deep learning, the algorithm looks for all thesetextures and wrinkles and shape changes on our face, and basically learns that all smileshave common characteristics, all smirks have subtlydifferent characteristics. And the next time it sees a new face, it essentially learns that this face has the samecharacteristics of a smile, and it says, 'Aha, I recognize this.This is a smile expression.'

So the best way to demonstratehow this technology works is to try a live demo, so I need a volunteer,preferably somebody with a face. (Laughter) Cloe's going to be our volunteer today.

So over the past five years, we've movedfrom being a research project at MIT to a company, where my team has worked really hardto make this technology work, as we like to say, in the wild. And we've also shrunk it so thatthe core emotion engine works on any mobile devicewith a camera, like this iPad. So let's give this a try.

Professional Way To Say Poker Face

As you can see, the algorithmhas essentially found Cloe's face, so it's this white bounding box, and it's tracking the mainfeature points on her face, so her eyebrows, her eyes,her mouth and her nose. The question is,can it recognize her expression? So we're going to test the machine. So first of all, give me your poker face.Yep, awesome. (Laughter) And then as she smiles,this is a genuine smile, it's great. So you can see the green bargo up as she smiles. Now that was a big smile. Can you try a subtle smileto see if the computer can recognize? It does recognize subtle smiles as well. We've worked really hardto make that happen. And then eyebrow raised,indicator of surprise. Brow furrow, which isan indicator of confusion. Frown. Yes, perfect. So these are all the differentaction units. There's many more of them. This is just a slimmed-down demo. But we call each readingan emotion data point, and then they can fire togetherto portray different emotions. So on the right side of the demo —look like you're happy. So that's joy. Joy fires up. And then give me a disgust face. Try to remember what it was likewhen Zayn left One Direction. (Laughter) Yeah, wrinkle your nose. Awesome. And the valence is actually quitenegative, so you must have been a big fan. So valence is how positiveor negative an experience is, and engagement is howexpressive she is as well. So imagine if Cloe had accessto this real-time emotion stream, and she could share itwith anybody she wanted to. Thank you. (Applause)

So, so far, we have amassed12 billion of these emotion data points. It's the largest emotiondatabase in the world. We've collected itfrom 2.9 million face videos, people who have agreedto share their emotions with us, and from 75 countries around the world. It's growing every day. It blows my mind away that we can now quantify somethingas personal as our emotions, and we can do it at this scale.

So what have we learned to date? Gender. Our data confirms somethingthat you might suspect. Women are more expressive than men. Not only do they smile more,their smiles last longer, and we can now really quantifywhat it is that men and women respond to differently. Let's do culture: So in the United States, women are 40 percentmore expressive than men, but curiously, we don't see any differencein the U.K. between men and women. (Laughter) Age: People who are 50 years and older are 25 percent more emotivethan younger people. Women in their 20s smile a lot morethan men the same age, perhaps a necessity for dating. But perhaps what surprised usthe most about this data is that we happento be expressive all the time, even when we are sittingin front of our devices alone, and it's not just when we're watchingcat videos on Facebook. We are expressive when we're emailing,texting, shopping online, or even doing our taxes.

Where is this data used today? In understanding how we engage with media, so understanding viralityand voting behavior; and also empoweringor emotion-enabling technology, and I want to share some examplesthat are especially close to my heart. Emotion-enabled wearable glassescan help individuals who are visually impairedread the faces of others, and it can help individualson the autism spectrum interpret emotion, something that they really struggle with. In education, imagineif your learning apps sense that you're confused and slow down, or that you're bored, so it's sped up, just like a great teacherwould in a classroom. What if your wristwatch tracked your mood, or your car sensed that you're tired, or perhaps your fridgeknows that you're stressed, so it auto-locks to prevent youfrom binge eating. (Laughter) I would like that, yeah. What if, when I was in Cambridge, I had access to my real-timeemotion stream, and I could share that with my familyback home in a very natural way, just like I would've if we were allin the same room together?

I think five years down the line, all our devices are goingto have an emotion chip, and we won't remember what it was likewhen we couldn't just frown at our device and our device would say, 'Hmm,you didn't like that, did you?' Our biggest challenge is that there areso many applications of this technology, my team and I realize that we can'tbuild them all ourselves, so we've made this technology availableso that other developers can get building and get creative. We recognize thatthere are potential risks and potential for abuse, but personally, having spentmany years doing this, I believe that the benefits to humanity from having emotionallyintelligent technology far outweigh the potential for misuse. And I invite you all to bepart of the conversation. The more people who knowabout this technology, the more we can all have a voicein how it's being used. So as more and moreof our lives become digital, we are fighting a losing battletrying to curb our usage of devices in order to reclaim our emotions. So what I'm trying to do insteadis to bring emotions into our technology and make our technologies more responsive. So I want those devicesthat have separated us to bring us back together. And by humanizing technology,we have this golden opportunity to reimagine how weconnect with machines, and therefore, how we, as human beings, connect with one another.

Thank you.

(Applause)

noun

an expressionless face: He can tell a funny story with a poker face.

a person who has or deliberately assumes a poker face: It is impossible to tell what that poker face is really thinking.

QUIZZES

CAN YOU IDENTIFY LITERATURE’S FAMOUS OPENING LINES?

The opening line of any book should say, in the words of Stephen King, “Listen. Come in here. You want to know about this.” Right? So intriguing! But, alas, that is not how books begin. So, how well do you know the actual opening lines from some of literature's greatest novels? Correctly match these memorable openers with their works of fiction and consider yourself an excellent listener!

“If you really want to hear about it, the first thing you'll probably want to know is where I was born, and what my lousy childhood was like, and how my parents were occupied and all before they had me, and all that David Copperfield kind of crap, but I don't feel like going into it, if you want to know the truth.”

Origin ofpoker face

OTHER WORDS FROM poker face

poker-faced,adjective

Words nearby poker face

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