Quo Vadis RankList-based System in Face Recognition?

• URL: http://arxiv.org/abs/2410.01498v1
• Date: Wed, 2 Oct 2024 12:46:18 GMT
• Title: Quo Vadis RankList-based System in Face Recognition?
• Authors: Xinyi Zhang, Manuel Günther,
• Abstract summary: We show that through a reasonable Logit-Cohort Selection (LoCoS) the performance of RankList-based functions can be improved drastically. Experiments on two challenging face recognition datasets demonstrate the enhanced performance of our proposed method.
• Score: 8.695948189958814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face recognition in the wild has gained a lot of focus in the last few years, and many face recognition models are designed to verify faces in medium-quality images. Especially due to the availability of large training datasets with similar conditions, deep face recognition models perform exceptionally well in such tasks. However, in other tasks where substantially less training data is available, such methods struggle, especially when required to compare high-quality enrollment images with low-quality probes. On the other hand, traditional RankList-based methods have been developed that compare faces indirectly by comparing to cohort faces with similar conditions. In this paper, we revisit these RankList methods and extend them to use the logits of the state-of-the-art DaliFace network, instead of an external cohort. We show that through a reasonable Logit-Cohort Selection (LoCoS) the performance of RankList-based functions can be improved drastically. Experiments on two challenging face recognition datasets not only demonstrate the enhanced performance of our proposed method but also set the stage for future advancements in handling diverse image qualities.

Related papers

• OSDFace: One-Step Diffusion Model for Face Restoration [72.5045389847792]
  Diffusion models have demonstrated impressive performance in face restoration. We propose OSDFace, a novel one-step diffusion model for face restoration. Results demonstrate that OSDFace surpasses current state-of-the-art (SOTA) methods in both visual quality and quantitative metrics.
  arXiv  Detail & Related papers  (2024-11-26T07:07:48Z)
• Rank-based No-reference Quality Assessment for Face Swapping [88.53827937914038]
  The metric of measuring the quality in most face swapping methods relies on several distances between the manipulated images and the source image. We present a novel no-reference image quality assessment (NR-IQA) method specifically designed for face swapping.
  arXiv  Detail & Related papers  (2024-06-04T01:36:29Z)
• QGFace: Quality-Guided Joint Training For Mixed-Quality Face Recognition [2.8519768339207356]
  We propose a novel quality-guided joint training approach for mixed-quality face recognition. Based on quality partition, classification-based method is employed for HQ data learning. For the LQ images which lack identity information, we learn them with self-supervised image-image contrastive learning.
  arXiv  Detail & Related papers  (2023-12-29T06:56:22Z)
• Effective Adapter for Face Recognition in the Wild [72.75516495170199]
  We tackle the challenge of face recognition in the wild, where images often suffer from low quality and real-world distortions. Traditional approaches-either training models directly on degraded images or their enhanced counterparts using face restoration techniques-have proven ineffective. We propose an effective adapter for augmenting existing face recognition models trained on high-quality facial datasets.
  arXiv  Detail & Related papers  (2023-12-04T08:55:46Z)
• A Comparative Analysis of the Face Recognition Methods in Video Surveillance Scenarios [0.0]
  This study presents comparative benchmark tables for the state-of-art face recognition methods. We constructed a video surveillance dataset of face IDs with high age variance, intra-class variance (face make-up, beard, etc.) with native surveillance facial imagery data for evaluation. On the other hand, this work discovers the best recognition methods for different conditions like non-masked faces, masked faces, and faces with glasses.
  arXiv  Detail & Related papers  (2022-11-05T17:59:18Z)
• FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment [19.928262020265965]
  FaceQgen is a No-Reference Quality Assessment approach for face images. It generates a scalar quality measure related with the face recognition accuracy. It is trained from scratch using the SCface database.
  arXiv  Detail & Related papers  (2022-01-03T17:22:38Z)
• QMagFace: Simple and Accurate Quality-Aware Face Recognition [5.5284501467256515]
  We propose a simple and effective face recognition solution (QMag-Face) that combines a quality-aware comparison score with a recognition model based on a magnitude-aware angular margin loss. The experiments conducted on several face recognition databases and benchmarks demonstrate that the introduced quality-awareness leads to consistent improvements in the recognition performance.
  arXiv  Detail & Related papers  (2021-11-26T12:44:54Z)
• Joint Face Image Restoration and Frontalization for Recognition [79.78729632975744]
  In real-world scenarios, many factors may harm face recognition performance, e.g., large pose, bad illumination,low resolution, blur and noise. Previous efforts usually first restore the low-quality faces to high-quality ones and then perform face recognition. We propose an Multi-Degradation Face Restoration model to restore frontalized high-quality faces from the given low-quality ones.
  arXiv  Detail & Related papers  (2021-05-12T03:52:41Z)
• Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data [59.85605718477639]
  We present an approach to use unlabeled faces to learn generalizable face representations. Experimental results on unconstrained datasets show that a small amount of unlabeled data with sufficient diversity can lead to an appreciable gain in recognition performance.
  arXiv  Detail & Related papers  (2020-03-17T20:58:56Z)
• 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)

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.