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Auflistung nach Schlagwort "Face and gesture recognition"

1 - 5 von 5
  • Konferenzbeitrag
    Exploiting Face Recognizability with Early Exit Vision Transformers
    (BIOSIG 2023, 2023) Seth Nixon, Pietro Ruiu
    Face recognition with Deep Learning is generally approached as a problem of capacity. The field has seen progressively deeper, more complex models or larger, more highly variant datasets. However, the carbon footprint of machine learning is a concern. A real push is developing to reduce the energy consumption of machine learning as we strive for a more eco-friendly society. Lower energy consumption or compute budget is always desirable, if accuracy is not reduced below a usable level. We present an approach using the state of the art Vision Transformer and Early Exits for reducing compute budget without significantly affecting performance. We develop a system for face recognition and identification with a closed-set gallery and show that with a small reduction in performance, a reasonable reduction in compute cost can be obtained using our method.
  • Konferenzbeitrag
    Face Image De-identification Based on Feature Embedding for Privacy Protection
    (BIOSIG 2023, 2023) Goki Hanawa, Koichi Ito
    With the expansion of social networking services, a large number of face images have been disclosed on the Internet.Since face recognition makes it easy to collect face images of specific persons, the collected face images can be used to attack face recognition systems, such as spoofing attacks.Face image de-identification, which makes face recognition difficult without changing the appearance of the face image, is necessary for disclosing face images safely on the Internet.In this paper, we propose a face image de-identification method by embedding facial features of another person into a face image.The proposed method uses a convolutional neural network to generate a face image that can be recognized as that of another person while preserving the appearance of the face image.Through a set of experiments using a public face image dataset, we demonstrate that the proposed method preserves the appearance of face images and has high de-identification performance against unknown face recognition models compared to conventional methods.
  • Konferenzbeitrag
    Face verification explainability heatmap generation using a vision transformer
    (BIOSIG 2023, 2023) Ricardo Correia, Paulo L Correia
    Explainable Face Recognition (XFR) is a critical technology to support the large deployment of learning-based face recognition solutions. This paper aims at contributing to the more transparent usage of Vision Transformers (ViTs) for face verification (FV) tasks, by proposing a novel approach for generating FV explainability heatmaps, for both positive and negative decisions. The proposed solution leverages on the attention maps generated by a ViT and employs masking techniques to create masks based on the highlighted regions in the attention maps. These masks are applied to the pair of faces, and the masking technique with most impact on the decision is selected to be used to generate heatmaps for the probe-gallery pair of faces. These heatmaps offer valuable insights into the decision-making process, shedding light on the most important face regions for the verification outcome. The key novelty of this paper lies in the proposed approach for generating explainability heatmaps tailored for verification pairs in the context of ViT models, which combines the ViT attention maps regions of the probe-gallery pair to create masks that allow evaluating those region´s impact on the verification decision for both positive and negative decisions.
  • Konferenzbeitrag
    Facial image reconstruction and its influence to face recognition
    (BIOSIG 2023, 2023) Filip Pleško, Tomas Goldmann
    This paper focuses on reconstructing damaged facial images using GAN neural networks. In addition, the effect of generating the missing part of the face on face recognition is investigated. The main objective of this work is to observe whether it is possible to increase the accuracy of face recognition by generating missing parts while maintaining a low false accept rate (FAR). A new model for generating the missing parts of a face has been proposed. For face-based recognition, state-of-the-art solutions from the DeepFace library and the QMagFace solution have been used.
  • Konferenzbeitrag
    Robust Sclera Segmentation for Skin-tone Agnostic Face Image Quality Assessment
    (BIOSIG 2023, 2023) Wassim Kabbani, Christoph Busch
    Face image quality assessment (FIQA) is crucial for obtaining good face recognition performance. FIQA algorithms should be robust and insensitive to demographic factors. The eye sclera has a consistent whitish color in all humans regardless of their age, ethnicity and skin-tone. This work proposes a robust sclera segmentation method that is suitable for face images in the enrolment and the border control face recognition scenarios. It shows how the statistical analysis of the sclera pixels produces features that are invariant to skin-tone, age and ethnicity and thus can be incorporated into FIQA algorithms to make them agnostic to demographic factors.