BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions

• URL: http://arxiv.org/abs/2008.06021v1
• Date: Thu, 13 Aug 2020 17:22:46 GMT
• Title: BioMetricNet: deep unconstrained face verification through learning of metrics regularized onto Gaussian distributions
• Authors: Arslan Ali, Matteo Testa, Tiziano Bianchi, Enrico Magli
• Abstract summary: We present BioMetricNet, a novel framework for deep unconstrained face verification. The proposed approach does not impose any specific metric on facial features. It shapes the decision space by learning a latent representation in which matching and non-matching pairs are mapped onto clearly separated and well-behaved target distributions.
• Score: 25.00475462213752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present BioMetricNet: a novel framework for deep unconstrained face verification which learns a regularized metric to compare facial features. Differently from popular methods such as FaceNet, the proposed approach does not impose any specific metric on facial features; instead, it shapes the decision space by learning a latent representation in which matching and non-matching pairs are mapped onto clearly separated and well-behaved target distributions. In particular, the network jointly learns the best feature representation, and the best metric that follows the target distributions, to be used to discriminate face images. In this paper we present this general framework, first of its kind for facial verification, and tailor it to Gaussian distributions. This choice enables the use of a simple linear decision boundary that can be tuned to achieve the desired trade-off between false alarm and genuine acceptance rate, and leads to a loss function that can be written in closed form. Extensive analysis and experimentation on publicly available datasets such as Labeled Faces in the wild (LFW), Youtube faces (YTF), Celebrities in Frontal-Profile in the Wild (CFP), and challenging datasets like cross-age LFW (CALFW), cross-pose LFW (CPLFW), In-the-wild Age Dataset (AgeDB) show a significant performance improvement and confirms the effectiveness and superiority of BioMetricNet over existing state-of-the-art methods.

Related papers

• MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection [64.29452783056253]
  The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored. We propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities.
  arXiv  Detail & Related papers  (2024-09-15T13:08:59Z)
• Generalizable Facial Expression Recognition [41.639746139849564]
  SOTA facial expression recognition (FER) methods fail on test sets with domain gaps with the train set. Recent domain adaptation FER methods need to acquire labeled or unlabeled samples of target domains to fine-tune the FER model. This paper aims to improve the zero-shot generalization ability of FER methods on different unseen test sets using only one train set.
  arXiv  Detail & Related papers  (2024-08-20T07:48:45Z)
• Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization [31.333862320143968]
  Semi-supervised semantic segmentation aims to utilize limited labeled images and abundant unlabeled images to achieve label-efficient learning. We propose a novel framework called textttMaskMatch, which enables fine-grained locality learning to achieve better dense segmentation.
  arXiv  Detail & Related papers  (2023-12-14T03:28:53Z)
• Open-Set Face Recognition with Maximal Entropy and Objectosphere Loss [7.710785884166695]
  This work concentrates on watchlists, an open-set task that is expected to operate at a low False Positive Identification Rate. We introduce a compact adapter network that benefits from additional negative face images when combined with distinct cost functions. The proposed approach adopts pre-trained deep neural networks (DNNs) for face recognition as feature extractors.
  arXiv  Detail & Related papers  (2023-11-01T09:52:02Z)
• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• Adaptive Face Recognition Using Adversarial Information Network [57.29464116557734]
  Face recognition models often degenerate when training data are different from testing data. We propose a novel adversarial information network (AIN) to address it.
  arXiv  Detail & Related papers  (2023-05-23T02:14:11Z)
• Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition [94.56304526014875]
  We propose the first Source-Free Unsupervised Domain Adaptation (SFUDA) method for Facial Expression Recognition (FER) Our method exploits self-supervised pretraining to learn good feature representations from the target data. We validate the effectiveness of our method in four adaptation setups, proving that it consistently outperforms existing SFUDA methods when applied to FER.
  arXiv  Detail & Related papers  (2022-10-11T08:24:50Z)
• Exploiting Shape Cues for Weakly Supervised Semantic Segmentation [15.791415215216029]
  Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise class predictions with only image-level labels for training. We propose to exploit shape information to supplement the texture-biased property of convolutional neural networks (CNNs) We further refine the predictions in an online fashion with a novel refinement method that takes into account both the class and the color affinities.
  arXiv  Detail & Related papers  (2022-08-08T17:25:31Z)
• Federated Learning for Face Recognition with Gradient Correction [52.896286647898386]
  In this work, we introduce a framework, FedGC, to tackle federated learning for face recognition. We show that FedGC constitutes a valid loss function similar to standard softmax.
  arXiv  Detail & Related papers  (2021-12-14T09:19:29Z)

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.