Greedy Search for Descriptive Spatial Face Features

7 Jan 2017  ·  Caner Gacav, Burak Benligiray, Cihan Topal ·

Facial expression recognition methods use a combination of geometric and appearance-based features. Spatial features are derived from displacements of facial landmarks, and carry geometric information. These features are either selected based on prior knowledge, or dimension-reduced from a large pool. In this study, we produce a large number of potential spatial features using two combinations of facial landmarks. Among these, we search for a descriptive subset of features using sequential forward selection. The chosen feature subset is used to classify facial expressions in the extended Cohn-Kanade dataset (CK+), and delivered 88.7% recognition accuracy without using any appearance-based features.

PDF Abstract

Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Facial Expression Recognition (FER) Cohn-Kanade Sequential forward selection Accuracy 88.7% # 1

Methods


No methods listed for this paper. Add relevant methods here