An Improved K - Mode Algorithm for Facial Expression Image Clustering

DOI

10.2991/aiie-16.2016.65How to use a DOI?

Keywords

cluster; categorical values; expression recognition

Abstract

When recognizing facial expression sequences by discrete Hidden Markov Model, it is necessary to cluster the image frames into several observation states. Considering the complexity of face images in emotion expressing, we partition the whole face into several sub-regions to do clustering separately. Then the categorical values from clustering results of each sub-region are combined to do the further clustering. In the paper, we propose an improve K-mode clustering algorithm called K-frequency to do the category data clustering. Instead of using a simple 0-1 method to determine the similarity between different samples, the K-frequency statistics the frequency of each categorical values of an image in a given cluster and sums them as the similarity between a sample with this cluster. Experiment results on CK+ database show that the bi-level clustering method with K-frequency algorithm outperforms the Single-layer clustering using K-means and bi-level clustering with K-mode. The improved k-modes algorithm K-frequency is more robust than the original k-mode algorithm in dealing with isolated points.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xibin Jia
AU  - Jianming Yuan
AU  - Yujie Xiao
PY  - 2016/11
DA  - 2016/11
TI  - An Improved K - Mode Algorithm for Facial Expression Image Clustering
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 282
EP  - 285
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.65
DO  - 10.2991/aiie-16.2016.65
ID  - Jia2016/11
ER  -