Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark

• URL: http://arxiv.org/abs/2304.11685v1
• Date: Sun, 23 Apr 2023 15:29:26 GMT
• Title: Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark
• Authors: Magnus Falkenberg, Anders Bensen Ottsen, Mathias Ibsen, Christian Rathgeb
• Abstract summary: HDA-SynChildFaces consists of 1,652 subjects and a total of 188,832 images, each subject being present at various ages and with many different intra-subject variations. We evaluate the performance of various facial recognition systems on the generated database and compare the results of adults and children at different ages.
• Score: 3.4110993541168853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the need for a large-scale database of children's faces by using generative adversarial networks (GANs) and face age progression (FAP) models to synthesize a realistic dataset referred to as HDA-SynChildFaces. To this end, we proposed a processing pipeline that initially utilizes StyleGAN3 to sample adult subjects, which are subsequently progressed to children of varying ages using InterFaceGAN. Intra-subject variations, such as facial expression and pose, are created by further manipulating the subjects in their latent space. Additionally, the presented pipeline allows to evenly distribute the races of subjects, allowing to generate a balanced and fair dataset with respect to race distribution. The created HDA-SynChildFaces consists of 1,652 subjects and a total of 188,832 images, each subject being present at various ages and with many different intra-subject variations. Subsequently, we evaluates the performance of various facial recognition systems on the generated database and compare the results of adults and children at different ages. The study reveals that children consistently perform worse than adults, on all tested systems, and the degradation in performance is proportional to age. Additionally, our study uncovers some biases in the recognition systems, with Asian and Black subjects and females performing worse than White and Latino Hispanic subjects and males.

Related papers

• ChildDiffusion: Unlocking the Potential of Generative AI and Controllable Augmentations for Child Facial Data using Stable Diffusion and Large Language Models [1.1470070927586018]
  The framework is validated by rendering high-quality child faces representing ethnicity data, micro expressions, face pose variations, eye blinking effects, different hair colours and styles, aging, multiple and different child gender subjects in a single frame. The proposed method circumvents common issues encountered in generative AI tools, such as temporal inconsistency and limited control over the rendered outputs.
  arXiv  Detail & Related papers  (2024-06-17T14:37:14Z)
• SwinFace: A Multi-task Transformer for Face Recognition, Expression Recognition, Age Estimation and Attribute Estimation [60.94239810407917]
  This paper presents a multi-purpose algorithm for simultaneous face recognition, facial expression recognition, age estimation, and face attribute estimation based on a single Swin Transformer. To address the conflicts among multiple tasks, a Multi-Level Channel Attention (MLCA) module is integrated into each task-specific analysis. Experiments show that the proposed model has a better understanding of the face and achieves excellent performance for all tasks.
  arXiv  Detail & Related papers  (2023-08-22T15:38:39Z)
• A Comparative Study of Image-to-Image Translation Using GANs for Synthetic Child Race Data [1.6536018920603175]
  This work proposes the utilization of image-to-image transformation to synthesize data of different races and adjust the ethnicity of children's face data. We consider ethnicity as a style and compare three different Image-to-Image neural network based methods to implement Caucasian child data and Asian child data conversion.
  arXiv  Detail & Related papers  (2023-08-08T12:54:05Z)
• ChildGAN: Large Scale Synthetic Child Facial Data Using Domain Adaptation in StyleGAN [1.6536018920603175]
  ChildGAN is built by performing smooth domain transfer using transfer learning. The dataset comprises more than 300k distinct data samples. The results demonstrate that synthetic child facial data of high quality offers an alternative to the cost and complexity of collecting a large-scale dataset from real children.
  arXiv  Detail & Related papers  (2023-07-25T18:04:52Z)
• Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition [0.0]
  We present a benchmark dataset for children's face recognition, which is compiled similarly to the famous face recognition benchmarks LFW, CALFW, CPLFW, XQLFW and AgeDB. We also present a development dataset for adapting face recognition models for face images of children.
  arXiv  Detail & Related papers  (2023-01-13T22:19:44Z)
• Deep Adaptation of Adult-Child Facial Expressions by Fusing Landmark Features [0.6219950137166257]
  Deep convolutional neural networks show promising results in classifying facial expressions of adults. We propose deep adaptive FACial Expressions fusing BEtaMix SElected Landmark Features (FACE-BE-SELF) for adult-child expression classification.
  arXiv  Detail & Related papers  (2022-09-18T17:29:36Z)
• CIAO! A Contrastive Adaptation Mechanism for Non-Universal Facial Expression Recognition [80.07590100872548]
  We propose Contrastive Inhibitory Adaptati On (CIAO), a mechanism that adapts the last layer of facial encoders to depict specific affective characteristics on different datasets. CIAO presents an improvement in facial expression recognition performance over six different datasets with very unique affective representations.
  arXiv  Detail & Related papers  (2022-08-10T15:46:05Z)
• 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)
• 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)
• Enhancing Facial Data Diversity with Style-based Face Aging [59.984134070735934]
  In particular, face datasets are typically biased in terms of attributes such as gender, age, and race. We propose a novel, generative style-based architecture for data augmentation that captures fine-grained aging patterns. We show that the proposed method outperforms state-of-the-art algorithms for age transfer.
  arXiv  Detail & Related papers  (2020-06-06T21:53:44Z)
• Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study [67.3961439193994]
  We introduce the most comprehensive, large-scale dataset of facial images and videos to date. The data are manually annotated in terms of identity, exact age, gender and kinship. A method to debias network embeddings is introduced and tested on the proposed benchmarks.
  arXiv  Detail & Related papers  (2020-05-15T00:14:39Z)

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.