Reliable Detection of Doppelg\"angers based on Deep Face Representations

• URL: http://arxiv.org/abs/2201.08831v1
• Date: Fri, 21 Jan 2022 18:37:08 GMT
• Title: Reliable Detection of Doppelg\"angers based on Deep Face Representations
• Authors: Christian Rathgeb, Daniel Fischer, Pawel Drozdowski, Christoph Busch
• Abstract summary: We assess the impact of doppelg"angers on the HDA Doppelg"anger and Disguised Faces in The Wild databases. It is found that doppelg"anger image pairs yield very high similarity scores resulting in a significant increase of false match rates. We propose a doppelg"anger detection method which distinguishes doppelg"angers from mated comparison trials.
• Score: 14.832145647643848
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Doppelg\"angers (or lookalikes) usually yield an increased probability of false matches in a facial recognition system, as opposed to random face image pairs selected for non-mated comparison trials. In this work, we assess the impact of doppelg\"angers on the HDA Doppelg\"anger and Disguised Faces in The Wild databases using a state-of-the-art face recognition system. It is found that doppelg\"anger image pairs yield very high similarity scores resulting in a significant increase of false match rates. Further, we propose a doppelg\"anger detection method which distinguishes doppelg\"angers from mated comparison trials by analysing differences in deep representations obtained from face image pairs. The proposed detection system employs a machine learning-based classifier, which is trained with generated doppelg\"anger image pairs utilising face morphing techniques. Experimental evaluations conducted on the HDA Doppelg\"anger and Look-Alike Face databases reveal a detection equal error rate of approximately 2.7% for the task of separating mated authentication attempts from doppelg\"angers.

Related papers

• Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method [77.65459419417533]
  We put face forgery in a semantic context and define that computational methods that alter semantic face attributes are sources of face forgery. We construct a large face forgery image dataset, where each image is associated with a set of labels organized in a hierarchical graph. We propose a semantics-oriented face forgery detection method that captures label relations and prioritizes the primary task.
  arXiv  Detail & Related papers  (2024-05-14T10:24:19Z)
• Double Trouble? Impact and Detection of Duplicates in Face Image Datasets [7.092869001331781]
  Face image datasets intended for facial biometrics research were created via web-scraping. This work presents an approach to detect both exactly and nearly identical face image duplicates.
  arXiv  Detail & Related papers  (2024-01-25T11:10:13Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• Adaptive Face Recognition Using Adversarial Information Network [57.29464116557734]
  Face recognition models often degenerate when training data are different from testing data. We propose a novel adversarial information network (AIN) to address it.
  arXiv  Detail & Related papers  (2023-05-23T02:14:11Z)
• Benchmarking Human Face Similarity Using Identical Twins [5.93228031688634]
  The problem of distinguishing identical twins and non-twin look-alikes in automated facial recognition (FR) applications has become increasingly important. This work presents an application of one of the largest twin datasets compiled to date to address two FR challenges.
  arXiv  Detail & Related papers  (2022-08-25T01:45:02Z)
• Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues [81.15465149555864]
  We establish the first joint face spoofing and detection benchmark using both visual appearance and physiological r cues. To enhance the r periodicity discrimination, we design a two-branch physiological network using both facial powerful rtemporal signal map and its continuous wavelet transformed counterpart as inputs.
  arXiv  Detail & Related papers  (2022-08-10T15:41:48Z)
• Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification [0.0]
  This paper aims to recognize the occlusion of one of four types in face images. Various transfer learning methods were tested, and the results show that MobileNet V2 with Gated Recurrent Unit(GRU) performs better than any other Transfer Learning methods.
  arXiv  Detail & Related papers  (2022-05-15T07:13:33Z)
• DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification [19.20353547123292]
  Face identification (FI) is ubiquitous and drives many high-stake decisions made by law enforcement. State-of-the-art FI approaches compare two images by taking the cosine similarity between their image embeddings. Here, we propose a re-ranking approach that compares two faces using the Earth Mover's Distance on the deep, spatial features of image patches.
  arXiv  Detail & Related papers  (2021-12-07T22:04:53Z)
• Spoofing Detection on Hand Images Using Quality Assessment [21.58895176617405]
  This paper presents an anti-spoofing method toward hand biometrics. A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images. Ten quality metrics are measured from each sample for classification between original and fake hand image.
  arXiv  Detail & Related papers  (2021-10-22T10:06:53Z)
• DeepFake Detection Based on the Discrepancy Between the Face and its Context [94.47879216590813]
  We propose a method for detecting face swapping and other identity manipulations in single images. Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context. We describe a method which uses the recognition signals from our two networks to detect such discrepancies. Our method achieves state of the art results on the FaceForensics++, Celeb-DF-v2, and DFDC benchmarks for face manipulation detection, and even generalizes to detect fakes produced by unseen methods.
  arXiv  Detail & Related papers  (2020-08-27T17:04:46Z)

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.