CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition

• URL: http://arxiv.org/abs/2304.11668v1
• Date: Sun, 23 Apr 2023 14:33:24 GMT
• Title: CoReFace: Sample-Guided Contrastive Regularization for Deep Face Recognition
• Authors: Youzhe Song, Feng Wang
• Abstract summary: We propose Contrastive Regularization for Face recognition (CoReFace) to apply image-level regularization in feature representation learning. Specifically, we employ sample-guided contrastive learning to regularize the training with the image-image relationship directly. To integrate contrastive learning into face recognition, we augment embeddings instead of images to avoid the image quality degradation.
• Score: 3.1677775852317085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The discriminability of feature representation is the key to open-set face recognition. Previous methods rely on the learnable weights of the classification layer that represent the identities. However, the evaluation process learns no identity representation and drops the classifier from training. This inconsistency could confuse the feature encoder in understanding the evaluation goal and hinder the effect of identity-based methods. To alleviate the above problem, we propose a novel approach namely Contrastive Regularization for Face recognition (CoReFace) to apply image-level regularization in feature representation learning. Specifically, we employ sample-guided contrastive learning to regularize the training with the image-image relationship directly, which is consistent with the evaluation process. To integrate contrastive learning into face recognition, we augment embeddings instead of images to avoid the image quality degradation. Then, we propose a novel contrastive loss for the representation distribution by incorporating an adaptive margin and a supervised contrastive mask to generate steady loss values and avoid the collision with the classification supervision signal. Finally, we discover and solve the semantically repetitive signal problem in contrastive learning by exploring new pair coupling protocols. Extensive experiments demonstrate the efficacy and efficiency of our CoReFace which is highly competitive with the state-of-the-art approaches.

Related papers

• On Mask-based Image Set Desensitization with Recognition Support [46.51027529020668]
  We propose a mask-based image desensitization approach while supporting recognition. We exploit an interpretation algorithm to maintain critical information for the recognition task. In addition, we propose a feature selection masknet as the model adjustment method to improve the performance based on the masked images.
  arXiv  Detail & Related papers  (2023-12-14T14:26:42Z)
• Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning [59.548960057358435]
  We propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities. Our face forgery detection model learns to extract common discriminative features through eliminating the effect of forgery methods and facial identities.
  arXiv  Detail & Related papers  (2022-07-08T09:23:59Z)
• Real-centric Consistency Learning for Deepfake Detection [8.313889744011933]
  We tackle the deepfake detection problem through learning the invariant representations of both classes. We propose a novel forgery semantical-based pairing strategy to mine latent generation-related features. At the feature level, based on the centers of natural faces at the representation space, we design a hard positive mining and synthesizing method to simulate the potential marginal features.
  arXiv  Detail & Related papers  (2022-05-15T07:01:28Z)
• Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition using Unit-Class Loss and Cross-Modality Discriminator [0.43748379918040853]
  We propose an end-to-end framework for cross-modal face recognition. A novel Unit-Class Loss is proposed for preserving identity information while discarding modality information. The proposed network can be used to extract modality-independent vector representations or a matching-pair classification for test images.
  arXiv  Detail & Related papers  (2021-11-29T06:14:00Z)
• Just Noticeable Difference for Machine Perception and Generation of Regularized Adversarial Images with Minimal Perturbation [8.920717493647121]
  We introduce a measure for machine perception inspired by the concept of Just Noticeable Difference (JND) of human perception. We suggest an adversarial image generation algorithm, which iteratively distorts an image by an additive noise until the machine learning model detects the change in the image by outputting a false label. We evaluate the adversarial images generated by our algorithm both qualitatively and quantitatively on CIFAR10, ImageNet, and MS COCO datasets.
  arXiv  Detail & Related papers  (2021-02-16T11:01:55Z)
• Fully Unsupervised Person Re-identification viaSelective Contrastive Learning [58.5284246878277]
  Person re-identification (ReID) aims at searching the same identity person among images captured by various cameras. We propose a novel selective contrastive learning framework for unsupervised feature learning. Experimental results demonstrate the superiority of our method in unsupervised person ReID compared with the state-of-the-arts.
  arXiv  Detail & Related papers  (2020-10-15T09:09:23Z)
• Face Anti-Spoofing Via Disentangled Representation Learning [90.90512800361742]
  Face anti-spoofing is crucial to security of face recognition systems. We propose a novel perspective of face anti-spoofing that disentangles the liveness features and content features from images.
  arXiv  Detail & Related papers  (2020-08-19T03:54:23Z)
• 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)
• Exploiting Semantics for Face Image Deblurring [121.44928934662063]
  We propose an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks. We incorporate face semantic labels as input priors and propose an adaptive structural loss to regularize facial local structures. The proposed method restores sharp images with more accurate facial features and details.
  arXiv  Detail & Related papers  (2020-01-19T13:06:27Z)
• Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild [103.51604161298512]
  We propose an adversarial learning framework for the generation and recognition of multiple characters in an image. Our framework can be integrated into recent recognition methods to achieve new state-of-the-art recognition accuracy.
  arXiv  Detail & Related papers  (2020-01-13T12:41:42Z)

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.