How facial recognition technology keeps you safe

Summary

Grab utilizes facial recognition technology across its platform for driver authentication, passenger verification, and digital electronic Know Your Customer (e-KYC) processes. The core pipeline consists of image preprocessing through face detection and alignment, anti-spoofing checks, feature extraction into high-dimensional vector embeddings, and downstream verification or search. To counter spoof attacks like screen replays, synthetic moiré patterns are generated and cropped face patches are used during training and inference to focus on local structures rather than global semantic noise. Face verification challenges involving shallow ID datasets and masked faces are resolved using semi-Siamese training architectures and masked data augmentations.

Context

Verifying user and partner identities for ride safety, crime deterrence, and regulatory e-KYC compliance while handling challenges like spoofing, shallow datasets, and masked faces.

Approach / What changed

Grab built a multi-stage facial recognition pipeline that pairs patch-based anti-spoofing models trained on synthetic moiré patterns with semi-Siamese neural network architectures and masked image augmentation for robust feature extraction and verification.

Takeaways

• Splitting cropped face images into evenly distributed patches forces anti-spoofing models to learn subtle local features, such as moiré patterns, instead of global semantic biases.
• Semi-Siamese training mitigates model collapse on shallow datasets (many identities with few samples) by using non-identical backbone subnetworks to increase intra-class diversity.
• Incorporating masked selfies into training sets allows verification without mask removal while functioning as data augmentation that improves overall unmasked verification accuracy.

Related reading

• Grab · Jul 13, 2022

  How we automated FAQ responses at Grab

  Internal engineering on-call engineers at Grab spent substantial working hours handling repetitive questions in Slack channels, such as how-to inquiries and access permission requests. To resolve this without building an in-house tool, the team conducted an anonymized vendor comparison and selected OneBar through an employee voting process and a phased proof-of-concept. Initial rollouts were restricted by contract to 20 channels, leading the team to prioritize deployment based on Slack message volume and member counts. Populating the knowledge base required roughly a quarter of consistent updates alongside tech talks, while a targeted crowdsourcing campaign among new onboarders expanded the glossary and grew usage to approximately 3,000 users.

  Preeti Karkera

• Grab · May 23, 2024

  How we evaluated the business impact of marketing campaigns

  Grab needed a scalable, centralised way to evaluate the business impact of millions of marketing messages sent across expanding campaigns. Manual analysis by marketing analysts produced inconsistent results and failed to scale, leading to the creation of multi-touch attribution and controlled experiment methodologies. The team designed a Marketing attribution model (MAM) that credits touchpoints based on relevance and recency, while using stratified hold-out groups to assess single campaign lift against baseline metrics. Statistical significance calculations, implemented using the jStat library based on the central limit theorem, ensure observed business metric differences like gross merchandise value are not random. The system provides campaign managers with direct access to impact analyses while the team works on challenges surrounding smaller sample sizes and hold-out group constraints.

  Jie Zhang

• Grab · Sep 8, 2022

  Automatic rule backtesting with large quantities of data

  Evaluating new or modified risk rules previously required Grab analysts to run slow offline Presto queries, manually construct payloads, or run rules in shadow mode for days. To standardize and accelerate this workflow, Grab developed an automated backtesting system powered by an AWS EMR Spark pipeline. Historical events are continuously ingested via Kafka and a Kubernetes stream pipeline into S3 using Snappy-compressed Parquet. Users configure replay intervals and rule definitions directly in the rule engine UI, which triggers asynchronous Spark jobs through Amazon SQS and Lambda. This automated simulation replaces multi-week shadow mode runs and generates downloadable aggregation metrics on transactions, user counts, and treatment outcomes.

  Chao Wang

• Grab · Apr 9, 2024

  Grab Experiment Decision Engine - a Unified Toolkit for Experimentation

  Grab's Experimentation platform tests thousands of experimental variants weekly, but analyses were largely handled manually using disparate tools unsuited to multi-sided marketplace nuances. To standardize workflows and eliminate reliability issues, Grab developed the GrabX Decision Engine, an internally open-sourced Python toolkit integrated with its Automated Experiment Analysis application. The framework includes a Trusted Advisor component that automates power analysis and sample size estimation while adjusting for clustered standard errors across metric data types. It also bundles a post-experiment statistical toolbox executing default and custom evaluations, ranging from non-parametric tests to regression models that control for confounders and fixed effects. This unified platform institutionalizes experimentation best practices across teams, streamlining collaboration and ensuring consistent statistical inference.

  Ruike Zhang