Facial Expressions Tracking and Recognition: Database Protocols for Systems Validation and Evaluation

TL;DR

This paper introduces standardized protocols for facial data collection to create versatile databases, facilitating validation and testing of facial recognition systems in real-world conditions.

Contribution

It proposes a methodology and two protocols for capturing facial data under uncontrolled conditions, resulting in two new databases for system validation.

Findings

Created FdMiee database capturing diverse facial behaviors.

Developed FACIA database with multimodal data including speech and emotions.

Simplified validation process for facial recognition algorithms.

Abstract

Each human face is unique. It has its own shape, topology, and distinguishing features. As such, developing and testing facial tracking systems are challenging tasks. The existing face recognition and tracking algorithms in Computer Vision mainly specify concrete situations according to particular goals and applications, requiring validation methodologies with data that fits their purposes. However, a database that covers all possible variations of external and factors does not exist, increasing researchers' work in acquiring their own data or compiling groups of databases. To address this shortcoming, we propose a methodology for facial data acquisition through definition of fundamental variables, such as subject characteristics, acquisition hardware, and performance parameters. Following this methodology, we also propose two protocols that allow the capturing of facial behaviors under uncontrolled and real-life situations. As validation, we executed both protocols which lead to creation of two sample databases: FdMiee (Facial database with Multi input, expressions, and environments) and FACIA (Facial Multimodal database driven by emotional induced acting). Using different types of hardware, FdMiee captures facial information under environmental and facial behaviors variations. FACIA is an extension of FdMiee introducing a pipeline to acquire additional facial behaviors and speech using an emotion-acting method. Therefore, this work eases the creation of adaptable database according to algorithm's requirements and applications, leading to simplified validation and testing processes.