Internship - Compression and Optimization of Face Recognition Models

il y a 1 semaine

Courbevoie, Île-de-France IDEMIA Public Security Temps plein

Job description:

IDEMIA develops high-performance face recognition models used worldwide in applications such as law enforcement, border control, and access control. Our technologies range from large, NIST-evaluated architectures to compact architectures embedded in sensors and constrained devices.

As model sizes and accuracy requirements increase, improving deployment efficiency has become a key challenge. This internship focuses on exploring modern 
model compression
 techniques to make our largest models lighter, faster, and easier to deploy on resource-limited platforms.

The objective is to design, implement, and evaluate these methods in order to produce 
highly efficient face recognition models
 that maintain strong accuracy while significantly reducing size, latency, and memory footprint.

Key Missions

The intern will work on compressing large face recognition models using approaches such as:

  • Pruning

to reduce computation and parameter count

  • Quantization

(INT8/INT4) to optimize memory and inference speed

  • Knowledge Distillation
     to transfer performance from high-accuracy models to smaller ones.

  • Low-Rank Adaptation
     as a lightweight mechanism to adapt existing models

Profile description:

Education:
 Engineering student from a prestigious school in the final year of the engineering cycle, or a Master's student (M2) specializing in computer vision, image processing, or Deep Learning.

Required Technical Skills:

  • Strong background in mathematics, data analysis, C, Python, and PyTorch
  • Ability to handle large-scale data and work in a research environment (exploring unfamiliar domains)
  • Knowledge in biometrics is a plus

Soft Skills & Languages:

  • Curious
  • Proactive and autonomous
  • Results- and solutions-oriented
  • Clear and convincing communication
  • Fluent spoken and written English

We offer:

What We Offer

  • High-impact projects in digital identity and public safety.
  • Continuous learning in a collaborative, innovative environment.

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