Will your Doorbell Camera still recognize you as you grow old

• URL: http://arxiv.org/abs/2308.04224v1
• Date: Tue, 8 Aug 2023 12:43:26 GMT
• Title: Will your Doorbell Camera still recognize you as you grow old
• Authors: Wang Yao, Muhammad Ali Farooq, Joseph Lemley and Peter Corcoran
• Abstract summary: This work explores the effect of age and aging on the performance of facial authentication methods. A photo-realistic age transformation method has been employed to augment a set of high-quality facial images with various age effects. The effect of these synthetic aging data on the high-performance deep-learning-based face recognition model is quantified.
• Score: 1.6536018920603175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robust authentication for low-power consumer devices such as doorbell cameras poses a valuable and unique challenge. This work explores the effect of age and aging on the performance of facial authentication methods. Two public age datasets, AgeDB and Morph-II have been used as baselines in this work. A photo-realistic age transformation method has been employed to augment a set of high-quality facial images with various age effects. Then the effect of these synthetic aging data on the high-performance deep-learning-based face recognition model is quantified by using various metrics including Receiver Operating Characteristic (ROC) curves and match score distributions. Experimental results demonstrate that long-term age effects are still a significant challenge for the state-of-the-art facial authentication method.

Related papers

• Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms [1.0499611180329804]
  We explore the feasibility of utilizing synthetic ageing data to improve the robustness of face recognition models. We show that the recognition rate of the model trained on synthetic ageing images is 3.33% higher than the results of the baseline model when tested on images with an age gap of 40 years.
  arXiv  Detail & Related papers  (2024-06-10T14:27:36Z)
• DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection [67.3143177137102]
  Deepfake detection refers to detecting artificially generated or edited faces in images or videos. We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
  arXiv  Detail & Related papers  (2023-12-07T07:19:45Z)
• 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)
• Identity-Preserving Aging of Face Images via Latent Diffusion Models [22.2699253042219]
  We propose, train, and validate the use of latent text-to-image diffusion models for synthetically aging and de-aging face images. Our models succeed with few-shot training, and have the added benefit of being controllable via intuitive textual prompting.
  arXiv  Detail & Related papers  (2023-07-17T15:57:52Z)
• Time flies by: Analyzing the Impact of Face Ageing on the Recognition Performance with Synthetic Data [18.47822752527376]
  This work studies the impact of ageing on the performance of an open-source biometric recognition system. The main findings indicate that short-term ageing in the range of 1-5 years has only minor effects on the general recognition performance.
  arXiv  Detail & Related papers  (2022-08-17T10:28:27Z)
• SynFace: Face Recognition with Synthetic Data [83.15838126703719]
  We devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the performance gap. We also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.
  arXiv  Detail & Related papers  (2021-08-18T03:41:54Z)
• FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild [50.8865921538953]
  We propose a method to explicitly incorporate facial semantics into age estimation. We design a face parsing-based network to learn semantic information at different scales. We show that our method consistently outperforms all existing age estimation methods.
  arXiv  Detail & Related papers  (2021-06-21T14:31:32Z)
• Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition [61.87842307164351]
  We first propose an Identity-Aware CycleGAN (IACycleGAN) model that applies a new perceptual loss to supervise the image generation network. It improves CycleGAN on photo-sketch synthesis by paying more attention to the synthesis of key facial regions, such as eyes and nose. We develop a mutual optimization procedure between the synthesis model and the recognition model, which iteratively synthesizes better images by IACycleGAN.
  arXiv  Detail & Related papers  (2021-03-30T01:30:08Z)
• PFA-GAN: Progressive Face Aging with Generative Adversarial Network [19.45760984401544]
  This paper proposes a novel progressive face aging framework based on generative adversarial network (PFA-GAN) The framework can be trained in an end-to-end manner to eliminate accumulative artifacts and blurriness. Extensively experimental results demonstrate superior performance over existing (c)GANs-based methods.
  arXiv  Detail & Related papers  (2020-12-07T05:45:13Z)
• Age Gap Reducer-GAN for Recognizing Age-Separated Faces [72.26969872180841]
  We propose a novel algorithm for matching faces with temporal variations caused due to age progression. The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification.
  arXiv  Detail & Related papers  (2020-11-11T16:43:32Z)

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.