# Impact of facial landmark localization on facial expression recognition

## TL;DR

This paper investigates how the accuracy of facial landmark localization affects facial expression recognition, revealing that current metrics do not always correlate with FER performance and proposing a new evaluation metric.

## Contribution

It introduces a new evaluation metric for facial landmark localization tailored to improve facial expression recognition outcomes.

## Key findings

- Current landmark accuracy does not guarantee better FER performance.
- Existing datasets lack proper difficulty level annotations.
- Proposed metric better correlates landmark quality with FER success.

## Abstract

Although facial landmark localization (FLL) approaches are becoming increasingly accurate for characterizing facial regions, one question remains unanswered: what is the impact of these approaches on subsequent related tasks? In this paper, the focus is put on facial expression recognition (FER), where facial landmarks are used for face registration, which is a common usage. Since the most used datasets for facial landmark localization do not allow for a proper measurement of performance according to the different difficulties (e.g., pose, expression, illumination, occlusion, motion blur), we also quantify the performance of recent approaches in the presence of head pose variations and facial expressions. Finally, a study of the impact of these approaches on FER is conducted. We show that the landmark accuracy achieved so far optimizing the conventional Euclidean distance does not necessarily guarantee a gain in performance for FER. To deal with this issue, we propose a new evaluation metric for FLL adapted to FER.