Similarity Guided Deep Face Image Retrieval

• URL: http://arxiv.org/abs/2107.05025v1
• Date: Sun, 11 Jul 2021 11:32:04 GMT
• Title: Similarity Guided Deep Face Image Retrieval
• Authors: Young Kyun Jang, Nam Ik Cho
• Abstract summary: Similarity Guided Hashing (SGH) method considers self and pairwise-similarity simultaneously. SGH delivers state-of-the-art retrieval performance on large scale face image dataset.
• Score: 21.99902461562925
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Face image retrieval, which searches for images of the same identity from the query input face image, is drawing more attention as the size of the image database increases rapidly. In order to conduct fast and accurate retrieval, a compact hash code-based methods have been proposed, and recently, deep face image hashing methods with supervised classification training have shown outstanding performance. However, classification-based scheme has a disadvantage in that it cannot reveal complex similarities between face images into the hash code learning. In this paper, we attempt to improve the face image retrieval quality by proposing a Similarity Guided Hashing (SGH) method, which gently considers self and pairwise-similarity simultaneously. SGH employs various data augmentations designed to explore elaborate similarities between face images, solving both intra and inter identity-wise difficulties. Extensive experimental results on the protocols with existing benchmarks and an additionally proposed large scale higher resolution face image dataset demonstrate that our SGH delivers state-of-the-art retrieval performance.

Related papers

• Quo Vadis RankList-based System in Face Recognition? [8.695948189958814]
  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.
  arXiv  Detail & Related papers  (2024-10-02T12:46:18Z)
• 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)
• Hierarchical Forgery Classifier On Multi-modality Face Forgery Clues [61.37306431455152]
  We propose a novel Hierarchical Forgery for Multi-modality Face Forgery Detection (HFC-MFFD) The HFC-MFFD learns robust patches-based hybrid representation to enhance forgery authentication in multiple-modality scenarios. The specific hierarchical face forgery is proposed to alleviate the class imbalance problem and further boost detection performance.
  arXiv  Detail & Related papers  (2022-12-30T10:54:29Z)
• Specialized Re-Ranking: A Novel Retrieval-Verification Framework for Cloth Changing Person Re-Identification [36.4001616893874]
  Re-ID can work under more complicated scenarios with higher security than normal Re-ID and biometric techniques. We propose a novel retrieval-verification framework to handle similar images.
  arXiv  Detail & Related papers  (2022-10-07T14:47:28Z)
• FaceMap: Towards Unsupervised Face Clustering via Map Equation [45.72096482531776]
  Face clustering is an essential task in computer vision due to the explosion of related applications such as augmented reality or photo album management. We develop an effective unsupervised method, named as FaceMap, by formulating face clustering as a process of non-overlapping community detection. Experiments with ablation studies demonstrate that FaceMap significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2022-03-21T03:23:09Z)
• Asymmetric Hash Code Learning for Remote Sensing Image Retrieval [22.91678927865952]
  We propose a novel deep hashing method, named asymmetric hash code learning (AHCL), for remote sensing image retrieval. The AHCL generates the hash codes of query and database images in an asymmetric way. The experimental results on three public datasets demonstrate that the proposed method outperforms symmetric methods in terms of retrieval accuracy and efficiency.
  arXiv  Detail & Related papers  (2022-01-15T07:00:38Z)
• Learning Contrastive Representation for Semantic Correspondence [150.29135856909477]
  We propose a multi-level contrastive learning approach for semantic matching. We show that image-level contrastive learning is a key component to encourage the convolutional features to find correspondence between similar objects.
  arXiv  Detail & Related papers  (2021-09-22T18:34:14Z)
• State of the Art: Image Hashing [0.0]
  Perceptual image hashing methods are often applied in various objectives, such as image retrieval, finding duplicate or near-duplicate images, and finding similar images from large-scale image content. The main challenge in image hashing techniques is robust feature extraction, which generates the same or similar hashes in images that are visually identical. In this article, we present a short review of the state-of-the-art traditional perceptual hashing and deep learning-based perceptual hashing methods, identifying the best approaches.
  arXiv  Detail & Related papers  (2021-08-26T13:53:30Z)
• Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition [90.26023388850771]
  This paper presents a deep neural network approach to extract decorrelation representations in a hyperspherical space for cross-domain face images. The proposed framework can be divided into two components: heterogeneous representation network and decorrelation representation learning. Experimental results on two challenging heterogeneous face databases show that our approach achieves superior performance on both verification and recognition tasks.
  arXiv  Detail & Related papers  (2020-05-25T07:01:12Z)
• 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)
• Fine-grained Image-to-Image Transformation towards Visual Recognition [102.51124181873101]
  We aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image. We adopt a model based on generative adversarial networks to disentangle the identity related and unrelated factors of an image. Experiments on the CompCars and Multi-PIE datasets demonstrate that our model preserves the identity of the generated images much better than the state-of-the-art image-to-image transformation models.
  arXiv  Detail & Related papers  (2020-01-12T05:26:47Z)

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.