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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Journal Article•
3,940 citations
15 Oct 2004
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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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01 Jun 2018
TL;DR: A two-stage training approach to utilize the small-scale training data provided by the Disguised Faces in the Wild competition to train Deep Convolutional Neural Networks for generic face recognition and Principal Components Analysis based on the DFW training set to find the best transformation matrix for identity representation of disguised faces.
Abstract: Recently, deep learning based approaches have yielded a significant improvement in face recognition in the wild. However," disguised face" recognition is still a challenging task that needs to be investigated, and the Disguised Faces in the Wild (DFW) competition is designed for this task. In this paper, we propose a two-stage training approach to utilize the small-scale training data provided by the DFW competition. Specifically, in the first stage, we train Deep Convolutional Neural Networks (DCNNs) for generic face recognition. In the second stage, we use Principal Components Analysis (PCA) based on the DFW training set to find the best transformation matrix for identity representation of disguised faces. We evaluate our model on the DFW testing dataset and it shows better performance over the state-of-the-art generic face recognition methods. It also achieves the best results on the DFW competition - Phase 1.
19 citations
Posted Content•
TL;DR: In this paper, the authors proposed a novel privacy-preserving neural network feature representation to suppress the sensitive information of a learned space while maintaining the utility of the data, which is based on an adversarial regularizer that introduces a sensitive information removal function in the learning objective.
Abstract: This work proposes a novel privacy-preserving neural network feature representation to suppress the sensitive information of a learned space while maintaining the utility of the data. The new international regulation for personal data protection forces data controllers to guarantee privacy and avoid discriminative hazards while managing sensitive data of users. In our approach, privacy and discrimination are related to each other. Instead of existing approaches aimed directly at fairness improvement, the proposed feature representation enforces the privacy of selected attributes. This way fairness is not the objective, but the result of a privacy-preserving learning method. This approach guarantees that sensitive information cannot be exploited by any agent who process the output of the model, ensuring both privacy and equality of opportunity. Our method is based on an adversarial regularizer that introduces a sensitive information removal function in the learning objective. The method is evaluated on three different primary tasks (identity, attractiveness, and smiling) and three publicly available benchmarks. In addition, we present a new face annotation dataset with balanced distribution between genders and ethnic origins. The experiments demonstrate that it is possible to improve the privacy and equality of opportunity while retaining competitive performance independently of the task.
19 citations
Posted Content•
TL;DR: A novel face recognition method, called a pairwise relational network (PRN), that obtains local appearance patches around landmark points on the feature map, and captures the pairwise relation between a pair ofLocal appearance patches to improve accuracy of face recognition.
Abstract: Existing face recognition using deep neural networks is difficult to know what kind of features are used to discriminate the identities of face images clearly. To investigate the effective features for face recognition, we propose a novel face recognition method, called a pairwise relational network (PRN), that obtains local appearance patches around landmark points on the feature map, and captures the pairwise relation between a pair of local appearance patches. The PRN is trained to capture unique and discriminative pairwise relations among different identities. Because the existence and meaning of pairwise relations should be identity dependent, we add a face identity state feature, which obtains from the long short-term memory (LSTM) units network with the sequential local appearance patches on the feature maps, to the PRN. To further improve accuracy of face recognition, we combined the global appearance representation with the pairwise relational feature. Experimental results on the LFW show that the PRN using only pairwise relations achieved 99.65% accuracy and the PRN using both pairwise relations and face identity state feature achieved 99.76% accuracy. On the YTF, both the PRN using only pairwise relations and the PRN using pairwise relations and the face identity state feature achieved the state-of-the-art (95.7% and 96.3%). The PRN also achieved comparable results to the state-of-the-art for both face verification and face identification tasks on the IJB-A, and the state-of-the-art on the IJB-B.
17 citations
01 May 2017
TL;DR: The proposed LDF-Net can achieve a frontal image where all pixels are from the original non-frontal image pixels and no invisible pixels exist, so as to maintain the informative information from the non- frontal images as much as possible.
Abstract: Face recognition is an important problem in computer vision, however, it is still challenging due to a few wild factors, such as large variations caused by pose, expression, lighting, etc. In this work, we mainly focus on dealing with the pose variations for face recognition. The proposed method attempts to directly transform a non-frontal face image into frontal one by Learning a Displacement Field network (LDFNet) and then recognizes with the transformed images. The existing methods, that follow the same scheme of transforming non-frontal faces into frontal ones, either transform by using 3D-model (3D methods) or transform by using 2D reconstructive methods (2D methods). The 3D methods may lead to the invisibility of some pixels in the transformed frontal images, while the 2D methods may lead to difference between the pixels in the transformed frontal images and the original non-frontal images. Our proposed LDF-Net method can handle these two problems by learning a morphable displacement field for each pixel in the transformed frontal image. Therefore, LDF-Net can achieve a frontal image where all pixels are from the original non-frontal image pixels and no invisible pixels exist, so as to maintain the informative information from the non-frontal images as much as possible. The experiments on MultiPIE dataset show that the proposed LDF-Net achieves state-of-theart performance for face recognition across pose, especially for those large poses.
17 citations
01 Oct 2018
TL;DR: A novel framework is proposed which transfers fundamental visual features learnt from a generic image dataset to supplement a supervised face recognition model which combines off-the-shelf supervised classifier and a generic, task independent network which encodes information related to basic visual cues such as color, shape, and texture.
Abstract: Plastic surgery and disguise variations are two of the most challenging co-variates of face recognition. The stateof-art deep learning models are not sufficiently successful due to the availability of limited training samples. In this paper, a novel framework is proposed which transfers fundamental visual features learnt from a generic image dataset to supplement a supervised face recognition model. The proposed algorithm combines off-the-shelf supervised classifier and a generic, task independent network which encodes information related to basic visual cues such as color, shape, and texture. Experiments are performed on IIITD plastic surgery face dataset and Disguised Faces in the Wild (DFW) dataset. Results showcase that the proposed algorithm achieves state of the art results on both the datasets. Specifically on the DFW database, the proposed algorithm yields over 87% verification accuracy at 1% false accept rate which is 53.8% better than baseline results com- puted using VGG Face.
16 citations