Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction

• URL: http://arxiv.org/abs/2301.11986v2
• Date: Wed, 11 Oct 2023 08:25:26 GMT
• Title: Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction
• Authors: Soroush Hashemifar, Abdolreza Marefat, Javad Hassannataj Joloudari and Hamid Hassanpour
• Abstract summary: We propose an approach named the Face Representation Augmentation (FRA) for augmenting face datasets. FRA is the first method that shifts its focus towards manipulating the face embeddings generated by any face representation learning algorithm. The proposed method improves the identity classification accuracies by 9.52 %, 10.04 %, and 16.60 %, in comparison with the base models of MagFace, ArcFace, and CosFace, respectively.
• Score: 8.493314424950599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The small amount of training data for many state-of-the-art deep learning-based Face Recognition (FR) systems causes a marked deterioration in their performance. Although a considerable amount of research has addressed this issue by inventing new data augmentation techniques, using either input space transformations or Generative Adversarial Networks (GAN) for feature space augmentations, these techniques have yet to satisfy expectations. In this paper, we propose an approach named the Face Representation Augmentation (FRA) for augmenting face datasets. To the best of our knowledge, FRA is the first method that shifts its focus towards manipulating the face embeddings generated by any face representation learning algorithm to create new embeddings representing the same identity and facial emotion but with an altered posture. Extensive experiments conducted in this study convince of the efficacy of our methodology and its power to provide noiseless, completely new facial representations to improve the training procedure of any FR algorithm. Therefore, FRA can help the recent state-of-the-art FR methods by providing more data for training FR systems. The proposed method, using experiments conducted on the Karolinska Directed Emotional Faces (KDEF) dataset, improves the identity classification accuracies by 9.52 %, 10.04 %, and 16.60 %, in comparison with the base models of MagFace, ArcFace, and CosFace, respectively.

Related papers

• Bridging the Gaps: Utilizing Unlabeled Face Recognition Datasets to Boost Semi-Supervised Facial Expression Recognition [5.750927184237346]
  We focus on utilizing large unlabeled Face Recognition (FR) datasets to boost semi-supervised FER. Specifically, we first perform face reconstruction pre-training on large-scale facial images without annotations. To further alleviate the scarcity of labeled and diverse images, we propose a Mixup-based data augmentation strategy.
  arXiv  Detail & Related papers  (2024-10-23T07:26:19Z)
• A visualization method for data domain changes in CNN networks and the optimization method for selecting thresholds in classification tasks [1.1118946307353794]
  Face Anti-Spoofing (FAS) has played a crucial role in preserving the security of face recognition technology. With the rise of counterfeit face generation techniques, the challenge posed by digitally edited faces to face anti-spoofing is escalating. We propose a visualization method that intuitively reflects the training outcomes of models by visualizing the prediction results on datasets.
  arXiv  Detail & Related papers  (2024-04-19T03:12:17Z)
• E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space [4.110419543591102]
  We propose a Generative Adversarial Network (GAN)-based model called Eyes-to-Face Network (E2F-Net) The proposed approach extracts identity and non-identity features from the periocular region using two dedicated encoders. We show that our method successfully reconstructs the whole face with high quality, surpassing current techniques.
  arXiv  Detail & Related papers  (2024-03-18T19:11:34Z)
• Survey on Deep Face Restoration: From Non-blind to Blind and Beyond [79.1398990834247]
  Face restoration (FR) is a specialized field within image restoration that aims to recover low-quality (LQ) face images into high-quality (HQ) face images. Recent advances in deep learning technology have led to significant progress in FR methods.
  arXiv  Detail & Related papers  (2023-09-27T08:39:03Z)
• A Survey of Deep Face Restoration: Denoise, Super-Resolution, Deblur, Artifact Removal [177.21001709272144]
  Face Restoration (FR) aims to restore High-Quality (HQ) faces from Low-Quality (LQ) input images. This paper comprehensively surveys recent advances in deep learning techniques for face restoration.
  arXiv  Detail & Related papers  (2022-11-05T07:08:15Z)
• Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination [68.78903256687697]
  In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal. Recent methods attempting to fill the gap via synthesis have achieved promising results, but their performance is still limited by the scarcity of paired training data. In this paper, we propose a new face hallucination paradigm for HFR, which not only enables data-efficient synthesis but also allows to scale up model training without breaking any privacy policy.
  arXiv  Detail & Related papers  (2022-03-30T20:44:33Z)
• The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition [71.24825724518847]
  Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. We formalize the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We demonstrate how our model achieves a comparable, if not better, performance to the current state-of-the-art in FER.
  arXiv  Detail & Related papers  (2020-09-15T09:25:37Z)
• DotFAN: A Domain-transferred Face Augmentation Network for Pose and Illumination Invariant Face Recognition [94.96686189033869]
  We propose a 3D model-assisted domain-transferred face augmentation network (DotFAN) DotFAN can generate a series of variants of an input face based on the knowledge distilled from existing rich face datasets collected from other domains. Experiments show that DotFAN is beneficial for augmenting small face datasets to improve their within-class diversity.
  arXiv  Detail & Related papers  (2020-02-23T08:16:34Z)
• Joint Deep Learning of Facial Expression Synthesis and Recognition [97.19528464266824]
  We propose a novel joint deep learning of facial expression synthesis and recognition method for effective FER. The proposed method involves a two-stage learning procedure. Firstly, a facial expression synthesis generative adversarial network (FESGAN) is pre-trained to generate facial images with different facial expressions. In order to alleviate the problem of data bias between the real images and the synthetic images, we propose an intra-class loss with a novel real data-guided back-propagation (RDBP) algorithm.
  arXiv  Detail & Related papers  (2020-02-06T10:56:00Z)
• Boosting Deep Face Recognition via Disentangling Appearance and Geometry [33.196270681809395]
  We propose a framework for disentangling the appearance and geometry representations in the face recognition task. We generate geometrically identical faces by incorporating spatial transformations. We show that the proposed approach enhances the performance of deep face recognition models.
  arXiv  Detail & Related papers  (2020-01-13T23:19:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.