590AC_WK2_Lec_2_Sensing_Recognition_Part2(3).ppt

590AC_WK2_Lec_2_Sensing_Recognition_Part2(3).ppt - CMPSCI...

Info icon This preview shows pages 1–12. Sign up to view the full content.

View Full Document Right Arrow Icon
CMPSCI 590AC 2-2 1 590AC 2-2 Week 2: Lecture 2 Sensing & Recognition of Emotions by Machines (Part 2) July 20, 2016
Image of page 1

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
CMPSCI 590AC 2-2 2 Readings (same as Part 1) Picard, 1997; “Affective Computing” Overview of affective signals and systems Chapter 5 T echniques for emotion sensing & recognition, synthesis & expression Chapters 6 & 7 Framework for organizing techniques for emotion sensing, recognition & expression Hudlicka, 2005: Affect Sensing and Recognition: State-of-the- Art Overview (on B’board) Picard, 2000. Toward computers that recognize and respond to user emotion. IBM Systems Journal, 39 (3-4), 705-719. (on B’board)
Image of page 2
CMPSCI 590AC 2-2 3 Optional Readings (same as Part 1) There are a number of optional supplemental readings (see updated syllabus) You are not expected to read these, but I encourage you to glance at the papers, to get a sense of the detailed processes involved in emotion recognition
Image of page 3

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
CMPSCI 590AC 2-2 4 Homework Homework #3: (Due 7-22, 8am) Posted on B’board in the “Homeworks” folder Email me with any questions & feel free to post questions & thoughts on the Discussion board
Image of page 4
CMPSCI 590AC 2-2 5 Outline for Lectures 1 & 2 Overview of emotion recognition & its applications Common framework for emotion recognition & expression Multi-modal emotion signatures Semantic primitives for different expressive channels Emotion recognition in detail Sensors & data filtering Extraction of features & semantic primitives Pattern recognition & classification algorithms Ethical issues Summary & Challenges
Image of page 5

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
CMPSCI 590AC 2-2 6 Lecture 1 (7-18) Covered… Overview of emotion recognition & its applications Common framework for emotion recognition & expression Multi-modal emotion signatures Semantic primitives for different expressive channels
Image of page 6
By now you have a sense of the multi-modal signatures of emotions… and the semantic primitives (features) associated with each expressive channel Now let’s get back to how machines can recognize emotions from these signatures and features
Image of page 7

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
CMPSCI 590AC 2-2 8 Outline for Lecture 2 (7-20) Emotion recognition in detail Sensors & data filtering Extraction of features & semantic primitives Pattern recognition & classification algorithms Ethical issues Summary & Challenges
Image of page 8
CMPSCI 590AC 2-2 9 Emotion Signals and Sensors
Image of page 9

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
CMPSCI 590AC 2-2 10 Sensors & Data Filtering Picard et al., 2008 BodyMedia SenseWear - Photo from Lisetti, 2003) Emotive Systems EEG Cap
Image of page 10
CMPSCI 590AC 2-2 11 Consider the Following in Signal & Sensor Selection Signal Does it reflect the emotion(s) of interest? Is it a component of a unique signature? Does it uniquely reflect a particular channel? Semantic primitive? Quality - Is it stable? Low noise? Sensor Ease of use / training requirements – Cost Is it capable of non-intrusive detection?
Image of page 11

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 12
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern