# SeekSuspect

> Partial-memory suspect recognition

- **Role:** Research Intern
- **Company:** MIDAS Lab · IIIT Delhi
- **Period:** 2022 – 2023

Delhi Police often starts from partial witness memory. Built a ranking prototype that surfaces candidates from incomplete facial descriptions for human review.

Witnesses give partial features, jawline, hair, age, not mugshots. Standard face recognition assumes complete probes; operational review needs ranked candidates without false-positive escalation.

## Problem

Delhi Police investigations often start from **partial witness memory**: jawline, hair texture, approximate age. Standard face matchers assume complete probes; a bad rank-one hit creates operational risk.

**Take home:** Research prototype with **Delhi Police** and MIDAS Lab; ranked candidates for human review.

## What I did

### Partial-feature embeddings

- Built embedding methods for incomplete facial descriptions and sketch-like witness inputs.
- Ranking pipeline from partial probes to a candidate gallery with confidence thresholds for analyst review.

### Evaluation with stakeholders

- Co-designed evaluation protocol with Delhi Police, what counts as a useful shortlist vs. a dangerous false lead.
- Documented failure modes and review thresholds before any field consideration.

## Team

- Devansh Gupta (PhD · USC Viterbi · Collaborator)
- Shikhar Sharma (Collaborator)

## Impact

Research prototype with law-enforcement stakeholders, not production deployment.

- Partial-memory recognition methodology documented with MIDAS Lab
- Operational failure modes and review thresholds defined with Delhi Police

## Sources

- [MIDAS Lab · IIIT Delhi](https://midas.iiitd.edu.in/)

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