27 min listen
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
ratings:
Length:
32 minutes
Released:
Jan 31, 2018
Format:
Podcast episode
Description
This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering technique called Stochastic Neighborhood Embedding. We discuss the concept of recognizing facial emotions in images including applications to problems such as: improving online communication quality, identifying suspicious individuals such as terrorists using video cameras, improving lie detector tests, improving athletic performance by providing emotion feedback, and designing smart advertising which can look at the customer’s face to determine if they are bored or interested and dynamically adapt the advertising accordingly. To address this problem we review clustering algorithm methods including K-means clustering, Linear Discriminant Analysis, Spectral Clustering, and the relatively new technique of Stochastic Neighborhood Embedding (SNE) clustering. At the end of this podcast we provide a brief review of the classic machine learning text by Christopher Bishop titled “Pattern Recognition and Machine Learning”. Make sure to visit: www.learningmachines101.com to obtain free transcripts of this podcast and important supplemental reference materials!
Released:
Jan 31, 2018
Format:
Podcast episode
Titles in the series (85)
LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems: In this episode we discuss how to learn to solve constraint satisfaction inference problems. by Learning Machines 101