SpeakingFaces: A Large-Scale Multimodal Dataset of Voice Commands with Visual and Thermal Video Streams

TL;DR

SpeakingFaces is a large-scale, multimodal dataset combining thermal, visual, and audio data streams of faces, designed to advance machine learning research in biometric and speech recognition applications.

Contribution

The paper introduces SpeakingFaces, a comprehensive multimodal dataset with synchronized thermal, visual, and audio data from 142 subjects, supporting diverse research in human-computer interaction and biometrics.

Findings

Baseline gender classification using multimodal data demonstrated effectiveness.

Thermal-to-visual face translation showcases domain transfer capabilities.

Dataset enables robust research in noisy environments and multimodal recognition.

Abstract

We present SpeakingFaces as a publicly-available large-scale multimodal dataset developed to support machine learning research in contexts that utilize a combination of thermal, visual, and audio data streams; examples include human-computer interaction, biometric authentication, recognition systems, domain transfer, and speech recognition. SpeakingFaces is comprised of aligned high-resolution thermal and visual spectra image streams of fully-framed faces synchronized with audio recordings of each subject speaking approximately 100 imperative phrases. Data were collected from 142 subjects, yielding over 13,000 instances of synchronized data (~3.8 TB). For technical validation, we demonstrate two baseline examples. The first baseline shows classification by gender, utilizing different combinations of the three data streams in both clean and noisy environments. The second example consists of thermal-to-visual facial image translation, as an instance of domain transfer.