End2End Occluded Face Recognition by Masking Corrupted Features

Qiu, Haibo; Gong, Dihong; Li, Zhifeng; Liu, Wei; Tao, Dacheng

doi:10.1109/tpami.2021.3098962

Cited by 73 publications

(61 citation statements)

References 39 publications

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“…Qiu et al [166] proposed a face recognition method with occlusions based on a single end-to-end deep neural network, called Face Recognition with Occlusion Masks (FROM). It is used to learn accurate feature masks, to discover the corrupted features using deep CNNs, and then to clean them with dynamically learned masks.…”

Section: Occluded Face Recognitionmentioning

confidence: 99%

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

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A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR.

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Section: Occluded Face Recognitionmentioning

confidence: 99%

Masked Face Recognition Using Deep Learning: A Review

et al. 2021

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“…Face Identification under Occlusion Partial occlusion presents a significant, ill-posed challenge to face identification as the AI has to rely only on incomplete or noisy facial features to make decisions [43]. Most prior methods propose to improve FI robustness by augmenting the training set of deep feature extractors with partially-occluded faces [23,41,56,58,62,62].…”

Section: Related Workmentioning

confidence: 99%

“…A common alternative is to learn to generate a spatial feature mask [36,43,51,57] or an attention map [62] to exclude the occluded (i.e. uninformative or noisy) regions in the input image from the face matching process.…”

Section: Related Workmentioning

confidence: 99%

“…Third, it is harder to identify when a face in the wild (e.g. from surveillance cameras) is occluded [43,53], distant or of a new type of photo (e.g. wearing masks in the COVID era) not included in both the training set of deep networks and the retrieval database-i.e., out-of-distribution (OOD) data.…”

Section: Introductionmentioning

confidence: 99%

“…For example, face verification accuracy may notoriously drop significantly (from 99.38% to 81.12% on LFW) given an occluded query face (Fig. 1b-d) [43] or adversarial queries [9,71].…”

Section: Introductionmentioning

confidence: 99%

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DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover's Distance Improves Out-Of-Distribution Face Identification

Phan¹,

Nguyen²

2021

Preprint

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Face identification (FI) is ubiquitous and drives many high-stake decisions made by the law enforcement. Stateof-the-art FI approaches compare two images by taking the cosine similarity between their image embeddings. Yet, such approach suffers from poor out-of-distribution (OOD) generalization to new types of images (e.g., when a query face is masked, cropped or rotated) not included in the training set or the gallery. 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. Our extra comparison stage explicitly examines image similarity at a fine-grained level (e.g., eyes to eyes) and is more robust to OOD perturbations and occlusions than traditional FI. Interestingly, without finetuning feature extractors, our method consistently improves the accuracy on all tested OOD queries: masked, cropped, rotated, and adversarial while obtaining similar results on in-distribution images.

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