A Study Shows The Effect Of Smiling On Trust Toward A Recommendation Agent

Trust is a significant factor in e-commerce businesses. Lui et al. (2010) conducted an experiment to determine whether smile of an online recommendation agent affects the trust of users. 

A recommendation agent is software that determines the preferences of users and recommends products and services in online shopping activities. 

Naturalistic avatar, a humanoid in form of human-like appearance, was used as a recommendation agent in this study. The trusting beliefs were decided as ‘competence’, ‘integrity’ and ‘benevolence’. An experimental website was developed for shopping a laptop by participants. They were told that they were supposed to choose a laptop for a friend who does not know much about laptops. Before making a choice, they interacted with a recommendation agent.

After making a final choice of laptop, the conclusions were drawn. The smiling agents were perceived as more competent than non-smiling agents; while smiling agents were not perceived to have higher integrity and more benevolence than non-smiling agents. 

The trust can be built using facial expression of a recommendation agent. So, while designing avatars as recommendations agents for websites, design a smiling agent to interact with people. It will make them believe they are interacting with competent avatars who recommend them a better product.

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Reference - Siu Man Lui, School of Business (Information Technology), James Cook University, Cairns, Australia. And Wendy Hui, The University of Nottingham, Ningbo, China. 

Research paper name - Effects of Smiling and Gender on Trust toward a Recommendation Agent.

Published in - International Conference on Cyberworlds (2010)

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(Image courtesy of Ambro at FreeDigitalPhotos.net)

Automated System For Detecting Asymmetric Spontaneous Smiles

Asymmetric smiles show contempt, disapproval, doubt and defiance. When we see a guy not able to solve a simple mathematical equation or not aware in which foreign country the prime minister’s will be; only one corner of lip goes up while smiling on his lack of common knowledge. This asymmetry indicates a strong negative emotion.

To develop a system detecting spontaneous asymmetric smiles as people watched online videos, Senechal et al. did a study including one-sided and asymmetric facial expressions. The right facial nerve is independent of left facial nerve. When it comes to lower part of face, the muscles are contralateral which makes us easier to perform asymmetric expressions such as smirk. As muscles of upper face are bilateral, performing asymmetric movements is difficult. Try to raise only one eyebrow like the famous eyebrow-raising of ‘The Rock’.

Researchers collected and labeled 2265 videos of spontaneous data of clips ranging from 30 seconds to a minute. They also collected 200 posed videos in participants were asked to pose various asymmetric facial expression with slight variations like tilting their head. The template matching technique was used to identify asymmetric smiles in spontaneous data clips.

For each frame of video, the face was located, scaled, cropped and flipped around vertical axis to train and test. It was found that training with combination of mixed and spontaneous data gave best results.

After training and testing, they achieved 69% precision in detection which can be interpreted as only 1 false detection of asymmetric smile in every 85 videos. This system can be used for automatic detection of spontaneous asymmetric smiles. This provides valuable understanding of how people take part while watching videos or films. The emotions they convey while watching a fiction or real film gives insights about human nature through different aspects.

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Reference - 

Researchers - Thibaud Senechal, Jay Turcot and Rana el Kaliouby. Affectiva Inc., Waltham,

MA, USA.

Research paper name - Smile or Smirk? Automatic Detection of Spontaneous Asymmetric

Smiles to Understand Viewer Experience.

Published in: Automatic Face and Gesture Recognition (FG), 2013. 10th IEEE International Conference.