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

Computer Science, Software Engineering

1-2 Master thesis students or 2 Bachelor thesis students

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.

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. 

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

You are valuable to us – how nice that you are interested in one of our proposals! There are a few things for you to keep in mind when applying.

• Applications are accepted in both Swedish and English, and you apply via the proposal advert.
• The announced thesis is open only to students affiliated with a Swedish University/College either directly or via an exchange program.
• When the thesis proposal states that it includes two students working together, we would like you to apply in pairs. In these cases, send one application each but make sure to clearly state in your application who your co-applicant is. If you have any questions regarding this, please do not hesitate to contact us.
• Please attach your CV and University/College grade summary.

Marie Åkesson on +46 272 1800 or marie.akesson@axis.com

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.

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.

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.

For more information about Axis, please visit our website www.axis.com.

Listen to Axis Communications Playlist