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Facial Expression Analysis Study for Online Shopping Behavior Prediction

tanek-rosales avatar
tanek-rosales
9 diapositives · 198 Views · Nov 01, 2014

This workshop overview presents a pilot study and main study on online shopping behavior, focusing on measuring facial expressions, movement, and shopper attention during various tasks. The pilot study involved 16 subjects engaging in different shopping tasks, while the main study implements learning algorithms to predict purchase decisions based on facial expressions, posture, and mouse movements. SVM inputs are sampled at 1/15th of a second, with output training for purchase decisions occurring at different time intervals. The study distinguishes between pre-purchase and pre-non-purchase behaviors both across and within shoppers.

#facial expression analysis study for online shopping behavior prediction #facial expression analysis study for online shopping behavior prediction ppt #powerpoint #presentation
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  1. DNP Workshop 12/04/06

  2. Overview • Pilot Study Description • Discussion of Main study

  3. Online Shopping Pilot Study • 16 subjects (8 male, 8 female) • 20 minutes of shopping • 4 types of tasks: • High involvement (e.g., car) • Low involvement (e.g., breakfast) • Arousing/Amusing (e.g., funny shirts) • Neutral (example) • Measured facial expressions: • Happy/sad • Amount of movement • Distance from camera (leaning in) • Attention (looking at camera) • Intention to purchase products

  4. Online Shopping Study • Example Faces:

  5. Pilot Shopping Study: Amount of Movement (7.6,9.9) (7.1,8.8) (5.1,3.0) (5.1,4.7) (7.5,9.1) (3.0,3.1) (6.0,6.9) (7.0,6.2) (4.2,3.5) (4.3,4.8) (3.3,4.4) (4.9,5.7) (5.4,4.5) (6.6,6.9) (8.7,5.0) (3.9,2.2) (2.6,1.4) (7.5,9.8) (5.7,6.2) (5.8,6.1) (6.0,7.6) (7.2,5.0)

  6. Main Study • Use Learning algorithsm • Output (things we are predicting) • Decision to purchase • Emotion • Input • Facial Expression • Posture • Mouse movement

  7. Purchase Face Classifier—Across Shoppers -1 = pre-purchase (s1-s20) 1 = pre-non-purchase (s21-s40) P1x…P1y…P2X…P2y…P3x…P3y…………….P53

  8. Purchase Face Classifier—Within shopper -1 = pre-purchase (s1-s40, timeX) 1 = pre-non-purchase (s1-s40, timeY) P1x…P1y…P2X…P2y…P3x…P3y…………….PN

  9. Main Study • SVM inputs are samples taken at 1/15th of a second • A time input sample includes 53 points (x, y of 22 points and 9 scalars due to orientation, and scale and aspect ration of eyes and mouth) • Output training is a 1 or a zero (1 is purchase) • Output training is a time sample N seconds after the input is taken (n = 1,3, or 5) • SVMs can be linear or nonlinear • Across or Within Shoppers