Deep Face Decoder : towards understanding the embedding space of convolutional networks through visual reconstruction of deep face templates

Križaj, Janez (author), Plesh, Richard O. (author), Banavar, Mahesh (author), Schuckers, Stephanie (author), Štruc, Vitomir (author)

Advances in deep learning and convolutional neural networks (ConvNets) have driven remarkable face recognition (FR) progress recently. However, the black-box nature of modern ConvNet-based face recognition models makes it challenging to interpret their decision-making process, to understand the reasoning behind specific success and failure cases, or to predict their responses to unseen data characteristics. It is, therefore, critical to design mechanisms that explain the inner workings of contemporary FR models and offer insight into their behavior. To address this challenge, we present in this paper a novel template-inversion approach capable of reconstructing high-fidelity face images from the embeddings (templates, feature-space representations) produced by modern FR techniques. Our approach is based on a novel Deep Face Decoder (DFD) trained in a regression setting to visualize the information encoded in the embedding space with the goal of fostering explainability. We utilize the developed DFD model in comprehensive experiments on multiple unconstrained face datasets, namely Visual Geometry Group Face dataset 2 (VGGFace2), Labeled Faces in the Wild (LFW), and Celebrity Faces Attributes Dataset High Quality (CelebA-HQ). Our analysis focuses on the embedding spaces of two distinct face recognition models with backbones based on the Visual Geometry Group 16-layer model (VGG-16) and the 50-layer Residual Network (ResNet-50). The results reveal how information is encoded in the two considered models and how perturbations in image appearance due to rotations, translations, scaling, occlusion, or adversarial attacks, are propagated into the embedding space. Our study offers researchers a deeper comprehension of the underlying mechanisms of ConvNet-based FR models, ultimately promoting advancements in model design and explainability.

• Language: English
• Keywords: deep face templates, template inversion, face recognition, deep learning, explainability, interpretability
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2024
• Number of pages: 20 str.
• Numbering: Vol. 132, art. 107941
• PID: 20.500.12556/RUL-156219
• UDC: 004.93
• ISSN on article: 1873-6769
• DOI: 10.1016/j.engappai.2024.107941
• COBISS.SI-ID: 184095491
• Publication date in RUL: 14.05.2024

Record is a part of a journal

• Title: Engineering applications of artificial intelligence
• Publisher: Elsevier, International Federation of Automatic Control
• ISSN: 1873-6769
• COBISS.SI-ID: 23000325

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: globoke obrazne predloge, inverzija predlog, prepoznavanje obrazov, globoko učenje, razložljivost, interpretabilnost