[2606.12074] Non-frontal face recognition using GANs and memristor-based classifiers

-->

Computer Science > Computer Vision and Pattern Recognition

arXiv:2606.12074 (cs)

[Submitted on 10 Jun 2026]

Title:Non-frontal face recognition using GANs and memristor-based classifiers

Authors:Semih Vazgecen, Cristian Sestito, Spyros Stathopoulos, Themis Prodromakis<br>View a PDF of the paper titled Non-frontal face recognition using GANs and memristor-based classifiers, by Semih Vazgecen and 3 other authors

View PDF<br>HTML (experimental)

Abstract:Face recognition systems have advanced significantly through deep learning techniques, delivering high performance and robustness in complex scenarios. However, these approaches incur substantial computational overhead, limiting their in situ applicability in resource-constrained platforms such as drones, where they can address challenges including non-frontal facial imagery. Memristor-based neuromorphic systems have emerged as a compelling approach for edge AI applications, combining biologically inspired processing with efficient and scalable computation. In this work, we propose a facial recognition framework that addresses non-frontal pose variations by integrating lightweight generative adversarial network (GAN)-based pose frontalisation with memristor-based neuromorphic recognition. The experimental results on two datasets demonstrate the effectiveness of combining adversarial learning with memristive technology, achieving up to 96% identification accuracy. The proposed approach alleviates the computational bottlenecks of conventional AI and offers a scalable, efficient solution for face recognition in dynamic real-world environments.

Comments:<br>12 pages, 4 figures, 1 Supplementary (22 pages, 16 figures, 6 tables, 4 supplementary notes)

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)

Cite as:<br>arXiv:2606.12074 [cs.CV]

(or<br>arXiv:2606.12074v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.12074

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Semih Vazgecen [view email]<br>[v1]<br>Wed, 10 Jun 2026 13:41:00 UTC (19,864 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled Non-frontal face recognition using GANs and memristor-based classifiers, by Semih Vazgecen and 3 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.CV

next >

new<br>recent<br>| 2026-06

Change to browse by:

cs<br>cs.AI<br>eess<br>eess.IV

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |<br>Disable MathJax (What is MathJax?)