# Greedy Search for Descriptive Spatial Face Features

## TL;DR

This paper introduces a method for selecting a subset of spatial facial features using greedy search, achieving high expression recognition accuracy without appearance features.

## Contribution

It proposes a sequential forward selection approach to identify the most descriptive spatial features from facial landmarks for expression recognition.

## Key findings

- Achieved 88.7% accuracy on CK+ dataset.
- Selected features effectively distinguish facial expressions.
- Method eliminates the need for appearance-based features.

## Abstract

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.