LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding

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: to obtain free transcripts of this podcast and important supplemental reference materials!

You can listen to LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding online on Radio and Podcast. Open the player on this page to stream the available audio.

LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding is an episode from Learning Machines 101 by Richard M. Golden.

This episode is 32:04 long.

This episode was published on Jan 31, 2018.

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