Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search

• URL: http://arxiv.org/abs/2009.13167v2
• Date: Tue, 29 Sep 2020 01:47:49 GMT
• Title: Video Face Recognition System: RetinaFace-mnet-faster and Secondary Search
• Authors: Qian Li, Nan Guo, Xiaochun Ye, Dongrui Fan, and Zhimin Tang
• Abstract summary: This paper mainly experiments complex faces in the video. We design an image pre-processing module for fuzzy scene or under-exposed faces to enhance images. We also propose RetinacFace-mnet-faster for detection and a confidence threshold specification for face recognition.
• Score: 5.371825910267909
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face recognition is widely used in the scene. However, different visual environments require different methods, and face recognition has a difficulty in complex environments. Therefore, this paper mainly experiments complex faces in the video. First, we design an image pre-processing module for fuzzy scene or under-exposed faces to enhance images. Our experimental results demonstrate that effective images pre-processing improves the accuracy of 0.11%, 0.2% and 1.4% on LFW, WIDER FACE and our datasets, respectively. Second, we propose RetinacFace-mnet-faster for detection and a confidence threshold specification for face recognition, reducing the lost rate. Our experimental results show that our RetinaFace-mnet-faster for 640*480 resolution on the Tesla P40 and single-thread improve speed of 16.7% and 70.2%, respectively. Finally, we design secondary search mechanism with HNSW to improve performance. Ours is suitable for large-scale datasets, and experimental results show that our method is 82% faster than the violent retrieval for the single-frame detection.

Related papers

• Analysis and Benchmarking of Extending Blind Face Image Restoration to Videos [99.42805906884499]
  We first introduce a Real-world Low-Quality Face Video benchmark (RFV-LQ) to evaluate leading image-based face restoration algorithms. We then conduct a thorough systematical analysis of the benefits and challenges associated with extending blind face image restoration algorithms to degraded face videos. Our analysis identifies several key issues, primarily categorized into two aspects: significant jitters in facial components and noise-shape flickering between frames.
  arXiv  Detail & Related papers  (2024-10-15T17:53:25Z)
• 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)
• SRMAE: Masked Image Modeling for Scale-Invariant Deep Representations [17.902523856490227]
  We propose to use image scale as a self-supervised signal for Masked Image Modeling (MIM) Our framework utilizes the latest advances in super-resolution (SR) to design the prediction head. Our method also achieves an accuracy of 74.84% on the task of recognizing low-resolution facial expressions, surpassing the current state-of-the-art FMD by 9.48%.
  arXiv  Detail & Related papers  (2023-08-17T09:43:14Z)
• Patch-wise Features for Blur Image Classification [3.762360672951513]
  Using our method we can discriminate between blur vs sharp image degradation. Experiments conducted on an open dataset show that the proposed low compute method results in 90.1% mean accuracy on the validation set. The proposed method is 10x faster than the VGG16 based model on CPU and scales linearly to the input image size making it suitable to be implemented on low compute edge devices.
  arXiv  Detail & Related papers  (2023-04-06T15:39:11Z)
• Octuplet Loss: Make Face Recognition Robust to Image Resolution [5.257115841810258]
  We propose a novel combination of the popular triplet loss to improve robustness against image resolution. We leverage the relationship between high-resolution images and their synthetically down-sampled variants jointly with their identity labels. Fine-tuning several state-of-the-art approaches with our method proves that we can significantly boost performance for cross-resolution (high-to-low resolution) face verification.
  arXiv  Detail & Related papers  (2022-07-14T08:22:58Z)
• LTT-GAN: Looking Through Turbulence by Inverting GANs [86.25869403782957]
  We propose the first turbulence mitigation method that makes use of visual priors encapsulated by a well-trained GAN. Based on the visual priors, we propose to learn to preserve the identity of restored images on a periodic contextual distance. Our method significantly outperforms prior art in both the visual quality and face verification accuracy of restored results.
  arXiv  Detail & Related papers  (2021-12-04T16:42:13Z)
• Projected GANs Converge Faster [50.23237734403834]
  Generative Adversarial Networks (GANs) produce high-quality images but are challenging to train. We make significant headway on these issues by projecting generated and real samples into a fixed, pretrained feature space. Our Projected GAN improves image quality, sample efficiency, and convergence speed.
  arXiv  Detail & Related papers  (2021-11-01T15:11:01Z)
• On the Effect of Selfie Beautification Filters on Face Detection and Recognition [53.561797148529664]
  Social media image filters modify the image contrast or illumination or occlude parts of the face with for example artificial glasses or animal noses. We develop a method to reconstruct the applied manipulation with a modified version of the U-NET segmentation network. From a recognition perspective, we employ distance measures and trained machine learning algorithms applied to features extracted using a ResNet-34 network trained to recognize faces.
  arXiv  Detail & Related papers  (2021-10-17T22:10:56Z)
• An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE [0.0]
  This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. This face recognition system can be employed for law enforcement purposes, where the surveillance camera captures a low-resolution image because of the distance of a person from the camera.
  arXiv  Detail & Related papers  (2021-04-15T04:54:29Z)
• Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks [0.5837881923712392]
  This paper mainly focuses on the restoration of motion-blurred faces. A deblurring method for motion-blurred facial image signals based on generative adversarial networks(GANs) is proposed. It uses an end-to-end method to train a sharp image generator, i.e., a processor for motion-blurred facial images.
  arXiv  Detail & Related papers  (2021-03-03T01:35:02Z)
• Recurrent Exposure Generation for Low-Light Face Detection [113.25331155337759]
  We propose a novel Recurrent Exposure Generation (REG) module and a Multi-Exposure Detection (MED) module. REG produces progressively and efficiently intermediate images corresponding to various exposure settings. Such pseudo-exposures are then fused by MED to detect faces across different lighting conditions.
  arXiv  Detail & Related papers  (2020-07-21T17:30:51Z)

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.