Thesis – Task-Aware Joint Optimization for Efficient Edge Face Recognition

Axis Communications AB; Lunds kommun; On-site; SEK 167,000 - 234,000; Full time

Job summary

Axis Communications AB in Lund, Sweden seeks a thesis student to develop a task-aware joint optimization framework for efficient edge face recognition. The project focuses on preserving identity-discriminative information while reducing image detail and processing overhead on resource-constrained edge devices.

The work targets a lightweight, quantized recognition network with FPGA-based edge system implementation, evaluating recognition reliability under varying imaging conditions and energy

Qualifications

• Master's or Bachelor student in Computer Science, Software Engineering, or a related field.

Responsibilities

• Develop a task-aware joint optimization framework for front-end processing and face recognition on resource-constrained edge devices.
• Evaluate recognition reliability, computational complexity, and energy consumption for FPGA-based edge systems.

Education

Master's or Bachelor student in Computer Science, Software Engineering, or a related field.

Job description

Job Title
Thesis – Task-Aware Joint Optimization for Efficient Edge Face Recognition

Job Title
Thesis – Task-Aware Joint Optimization for Efficient Edge Face Recognition
Job Description
Category
Computer Science, Software Engineering
Scope
1-2 Master thesis students or 2 Bachelor thesis students
Background
Face recognition in intercoms and access control systems requires reliable performance under varying imaging conditions while operating within limited computational and energy budgets. Recent studies have explored spectral convolution for efficient front-end processing directly in image sensors [1] and sub-byte quantization to reduce the computational requirements of mobile face-recognition networks [2]. However, the joint optimization of front-end processing parameters and face-recognition networks remains underexplored, particularly in terms of selecting task-relevant image information to balance recognition of reliability and computational efficiency.
Goal
The goal of this project is to develop a task-aware joint optimization framework for front-end processing and face recognition on resource-constrained edge devices. The front end will be optimized to preserve essential identity-discriminative information while reducing unnecessary image detail and processing overhead. In combination with a lightweight, quantized recognition network, the proposed approach will be evaluated in terms of recognition reliability under varying imaging conditions, computational complexity, and energy consumption, with implementation targeting FPGA-based edge systems.
Who are you?
Master's or Bachelor student in Computer Science, Software Engineering, or a related field.
Who to contact for any questions regarding the position!
Marie Åkesson on [phone] or [e-mail]
References
1 Cui, Kaiyu, et al. "Spectral convolutional neural network chip for in-sensor edge computing of incoherent natural light." Nature Communications 16.1 (2025): 81.
2 Bunda, Sebastian, Luuk Spreeuwers, and Chris Zeinstra. "Sub-byte quantization of mobile face recognition convolutional neural networks." 2022 International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE, 2022.
Type of Employment
Thesis Worker (Fixed Term)
Posting End Date
2026-11-27
Certain roles at Axis require background checks, which means applicable verifications will be done in these recruitments. Notice will be provided before we take any action.
About Axis Communications
We enable a smarter and safer world by creating innovative solutions for improving security and business performance. As a network technology company and industry leader, we offer solutions in video surveillance, access control, intercom, and audio systems, enhanced by intelligent analytics applications.
With around 6,000 committed employees in over 50 countries, we collaborate with partners worldwide. Together, we thrive in our friendly, open, and collaborative culture and inspire each other to think beyond the expected. United by our commitment to inclusion, diversity, and sustainability, we consistently seek to develop our skills and way of working.
Let´s create a smarter and safer world
For more information about Axis, please visit our website www.axis.com.
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