# Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing

## TL;DR

This paper introduces a domain adaptation approach using multi-channel data and autoencoders for robust face anti-spoofing, improving detection of sophisticated attacks across different datasets.

## Contribution

It proposes a novel autoencoder and MLP-based face PAD system that leverages multi-channel data and domain adaptation to enhance robustness against presentation attacks.

## Key findings

- Improved detection accuracy on multi-channel PAD database.
- Learning regional facial features enhances discriminative power.
- Domain adaptation effectively transfers knowledge across domains.

## Abstract

While the performance of face recognition systems has improved significantly in the last decade, they are proved to be highly vulnerable to presentation attacks (spoofing). Most of the research in the field of face presentation attack detection (PAD), was focused on boosting the performance of the systems within a single database. Face PAD datasets are usually captured with RGB cameras, and have very limited number of both bona-fide samples and presentation attack instruments. Training face PAD systems on such data leads to poor performance, even in the closed-set scenario, especially when sophisticated attacks are involved. We explore two paths to boost the performance of the face PAD system against challenging attacks. First, by using multi-channel (RGB, Depth and NIR) data, which is still easily accessible in a number of mass production devices. Second, we develop a novel Autoencoders + MLP based face PAD algorithm. Moreover, instead of collecting more data for training of the proposed deep architecture, the domain adaptation technique is proposed, transferring the knowledge of facial appearance from RGB to multi-channel domain. We also demonstrate, that learning the features of individual facial regions, is more discriminative than the features learned from an entire face. The proposed system is tested on a very recent publicly available multi-channel PAD database with a wide variety of presentation attacks.