Deep face recognition: A survey
TL;DR: A comprehensive review of the recent developments on deep face recognition can be found in this paper, covering broad topics on algorithm designs, databases, protocols, and application scenes, as well as the technical challenges and several promising directions.
About: This article is published in Neurocomputing.The article was published on 2021-03-14 and is currently open access. It has received 353 citations till now. The article focuses on the topics: Deep learning & Feature extraction.
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3,940 citations
15 Oct 2004
2,118 citations
TL;DR: This survey provides a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors, and lists the traditional and new applications.
Abstract: Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in people's life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning algorithms for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline thoroughly and deeply, in this survey, we analyze the methods of existing typical detection models and describe the benchmark datasets at first. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.
749 citations
TL;DR: In this article, the authors provide a review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions.
278 citations
01 Jan 2006
TL;DR: It is concluded that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work, and the efficacy of this algorithm is evaluated against the variables of gender and racial origin.
Abstract: This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.
139 citations
References
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16 Feb 2001
TL;DR: The Facial Recognition Vendor Test 2000 (FRVT 2000) as discussed by the authors was sponsored by the DoD Counterdrug Technology Development Program Office, National Institute of Justice and the Defense Advanced Research Projects Agency and was administered in May and June 2000.
Abstract: : The biggest change in the facial recognition community since the completion of the FERET program has been the introduction of facial recognition products to the commercial market. Open market competitiveness has driven numerous technological advances in automated face recognition since the FERET program and significantly lowered system costs. Today there are dozens of facial recognition systems available that have the potential to meet performance requirements for numerous applications. But which of these systems best meet the performance requirements for given applications? Repeated inquiries from numerous government agencies on the current state of facial recognition technology prompted the DoD Counterdrug Technology Development Program Office to establish a new set of evaluations. The Facial Recognition Vendor Test 2000 (FRVT 2000) was cosponsored by the DoD Counterdrug Technology Development Program Office, the National Institute of Justice and the Defense Advanced Research Projects Agency and was administered in May and June 2000.
49 citations
14 May 2019
TL;DR: Experimental results demonstrate that studied models suffer from a very structured and damaging demographic bias, and shine a light on novel testing protocols to appropriately validate the generalization capabilities of face recognition models.
Abstract: Although deep face recognition has achieved impressive results in recent years, controversy has arisen regarding racial and gender bias of the models, questioning their deployment into sensitive scenarios. This work quantifies for the first time the demographic imbalance of popular public face datasets in terms of identity, gender and ethnicity. We also publicly release DemogPairs, a new validation set with 10.8K facial images and 58.3M identity verification pairs, distributed in demographically-balanced folds of Asian, Black and White females and males. A benchmark of experiments is carried out using DemogPairs over state-of-the-art deep face recognition models (SphereFace, FaceNet and ResNet50), in order to analyze their cross-demographic behavior. Experimental results demonstrate that studied models suffer from a very structured and damaging demographic bias. Our experiments shine a light on novel testing protocols to appropriately validate the generalization capabilities of face recognition models.
49 citations
01 May 2017
TL;DR: This paper builds on a number of observations which allow rendering new 3D views of faces at a computational cost which is equivalent to simple 2D image warping, and shows that the run-time of an optimized OpenGL rendering engine is slower than the simple Python implementation for the same purpose.
Abstract: Recent work demonstrated that computer graphics techniques can be used to improve face recognition performances by synthesizing multiple new views of faces available in existing face collections. By so doing, more images and more appearance variations are available for training, thereby improving the deep models trained on these images. Similar rendering techniques were also applied at test time to align faces in 3D and reduce appearance variations when comparing faces. These previous results, however, did not consider the computational cost of rendering: At training, rendering millions of face images can be prohibitive; at test time, rendering can quickly become a bottleneck, particularly when multiple images represent a subject. This paper builds on a number of observations which, under certain circumstances, allow rendering new 3D views of faces at a computational cost which is equivalent to simple 2D image warping. We demonstrate this by showing that the run-time of an optimized OpenGL rendering engine is slower than the simple Python implementation we designed for the same purpose. The proposed rendering is used in a face recognition pipeline and tested on the challenging IJB-A and Janus CS2 benchmarks. Our results show that our rendering is not only fast, but improves recognition accuracy.
49 citations
TL;DR: This work studies the possibility of end-to-end face recognition through alignment learning in which neither prior knowledge on facial landmarks nor artificially defined geometric transformations are required.
Abstract: A common practice in modern face recognition methods is to specifically align the face area based on the prior knowledge of human face structure before recognition feature extraction. The face alignment is usually implemented independently, causing difficulties in the designing of end-to-end face recognition models. We study the possibility of end-to-end face recognition through alignment learning in which neither prior knowledge on facial landmarks nor artificially defined geometric transformations are required. Only human identity clues are used for driving the automatic learning of appropriate geometric transformations for the face recognition task. Trained purely on publicly available datasets, our model achieves a verification accuracy of 99.33% on the LFW dataset, which is on par with state-of-the-art single model methods.
49 citations
08 Sep 2018
TL;DR: Zhang et al. as discussed by the authors proposed a dependency-aware attention control (DAC) network, which resorts to actor-critic reinforcement learning for sequential attention decision of each image embedding to fully exploit the rich correlation cues among the unordered images.
Abstract: This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogeneous contents containing orderless images and videos. The importance of each image is usually considered either equal or based on their independent quality assessment. How to model the relationship of orderless images within a set remains a challenge. We address this problem by formulating it as a Markov Decision Process (MDP) in the latent space. Specifically, we first present a dependency-aware attention control (DAC) network, which resorts to actor-critic reinforcement learning for sequential attention decision of each image embedding to fully exploit the rich correlation cues among the unordered images. Moreover, we introduce its sample-efficient variant with off-policy experience replay to speed up the learning process. The pose-guided representation scheme can further boost the performance at the extremes of the pose variation.
48 citations