Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-Vision

TL;DR

This paper introduces a specialized face detection dataset derived from videos, utilizing threshold-based neuromorphic sensors to facilitate research in low-power, event-driven vision systems.

Contribution

It provides an annotated, multi-threshold dataset for face detection using neuromorphic sensors, along with tools to generate event data from raw videos, supporting the development of energy-efficient vision technologies.

Findings

Enables evaluation of neural architectures under different threshold conditions

Supports development of low-power, neuromorphic vision systems

Provides publicly available dataset and tools for the research community

Abstract

Smart focal-plane and in-chip image processing has emerged as a crucial technology for vision-enabled embedded systems with energy efficiency and privacy. However, the lack of special datasets providing examples of the data that these neuromorphic sensors compute to convey visual information has hindered the adoption of these promising technologies. Neuromorphic imager variants, including event-based sensors, produce various representations such as streams of pixel addresses representing time and locations of intensity changes in the focal plane, temporal-difference data, data sifted/thresholded by temporal differences, image data after applying spatial transformations, optical flow data, and/or statistical representations. To address the critical barrier to entry, we provide an annotated, temporal-threshold-based vision dataset specifically designed for face detection tasks derived from the same videos used for Aff-Wild2. By offering multiple threshold levels (e.g., 4, 8, 12, and 16), this dataset allows for comprehensive evaluation and optimization of state-of-the-art neural architectures under varying conditions and settings compared to traditional methods. The accompanying tool flow for generating event data from raw videos further enhances accessibility and usability. We anticipate that this resource will significantly support the development of robust vision systems based on smart sensors that can process based on temporal-difference thresholds, enabling more accurate and efficient object detection and localization and ultimately promoting the broader adoption of low-power, neuromorphic imaging technologies. To support further research, we publicly released the dataset at \url{https://dx.doi.org/10.21227/bw2e-dj78}.