Comparison between Euclidean and Manhattan distance measure for facial expressions classification

In this paper, we compare classification results, of six facial expressions including joy, surprise, sadness, anger, disgust, and fear, relying on two different methods of distance computing between 121 landmark points on the face. Facial features were computed using L1 norm (Manhattan distance) in...

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Bibliographic Details
Published in2017 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS) pp. 1 - 4
Main Authors Greche, Latifa, Jazouli, Maha, Es-Sbai, Najia, Majda, Aicha, Zarghili, Arsalane
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Biological neural networks
Emotion recognition
Euclidean distance
Face
Face recognition
facial expression recognition
kinect sensor
Manhattan distance
neural network
Neurons
Training
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Summary:In this paper, we compare classification results, of six facial expressions including joy, surprise, sadness, anger, disgust, and fear, relying on two different methods of distance computing between 121 landmark points on the face. Facial features were computed using L1 norm (Manhattan distance) in the first case and L2 norm (Euclidean distance) in the second case. Training and test data have been collected using kinect sensor. Labelled dataset contains sequences of 121 landmark points extracted from the face of each subject while displaying six facial expressions including joy, surprise, sadness, anger, disgust, and fear. Classification has been realized using multi-layer feed forward neural network with one hidden layer. Good recognition rates have been achieved in the early stages of training regarding Euclidean facial distances.
DOI:10.1109/WITS.2017.7934618