Recognition of facial expressions based on salient geometric features and support vector machines

TL;DR

This paper presents an automatic facial expression recognition system using salient geometric features extracted from tracked facial points, employing AdaBoost and ELM classifiers, evaluated on multiple datasets.

Contribution

The paper introduces a novel FER system that combines geometric feature extraction with AdaBoost and ELM, improving recognition accuracy across diverse datasets.

Findings

High recognition accuracy on CK+, MMI, and MUG datasets.

Effective use of geometric features from points, lines, and triangles.

Demonstrates the robustness of the proposed system across different data sets.

Abstract

Facial expressions convey nonverbal cues which play an important role in interpersonal relations, and are widely used in behavior interpretation of emotions, cognitive science, and social interactions. In this paper we analyze different ways of representing geometric feature and present a fully automatic facial expression recognition (FER) system using salient geometric features. In geometric feature-based FER approach, the first important step is to initialize and track dense set of facial points as the expression evolves over time in consecutive frames. In the proposed system, facial points are initialized using elastic bunch graph matching (EBGM) algorithm and tracking is performed using Kanade-Lucas-Tomaci (KLT) tracker. We extract geometric features from point, line and triangle composed of tracking results of facial points. The most discriminative line and triangle features are extracted using feature selective multi-class AdaBoost with the help of extreme learning machine (ELM) classification. Finally the geometric features for FER are extracted from the boosted line, and triangles composed of facial points. The recognition accuracy using features from point, line and triangle are analyzed independently. The performance of the proposed FER system is evaluated on three different data sets: namely CK+, MMI and MUG facial expression data sets.