Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images

Storey, Gary; Bouridane, Ahmed; Jiang, Richard

doi:10.1109/access.2018.2882227

Cited by 35 publications

(30 citation statements)

References 35 publications

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“…The convolution layer produces new feature maps from previous feature maps and acts as multiple learnable filters in the input image [17]. In traditional CNN, 1-D convolution is used at the convolutional layers to extract spectral features from the input data [32]. 2-D convolution is used at the convolutional layers to extract spatial features from the input data [33].…”

Section: A 3d-convolution Layermentioning

confidence: 99%

Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery

et al. 2020

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Cloud and cloud shadow detection is one of the most important tasks for optical remote sensing image preprocessing. It is not an easy task due to the variety and complexity of underlying surfaces, such as the low-albedo objects (water and mountain shadow) and the high-albedo objects (snow and ice). In this study, an end-to-end multiscale 3D-CNN method is proposed for cloud and cloud shadow detection in high resolution multispectral imagery. Specifically, a multiscale learning module is designed to extract cloud and cloud shadow contextual information of different levels. In order to make full use of band information, four band-combination images are inputted into the multiscale 3D-CNN. A joint spectral-spatial information of 3D-convolution layer is developed to fully explore the joint spatial-spectral correlations feature in the input data. Overall, in the experiments undertaken in this paper, the proposed method achieved a mean overall accuracy of 97.27% for cloud detection, with a mean precision of 96.02% and a mean recall of 95.86%. For cloud shadow detection, the proposed method achieved a mean precision of 95.92% and a mean recall of 92.86%. Experimental results on two validation datasets (GF-1 WFV validation data and ZY-3 validation data) show that the proposed multiscale-3D-CNN method achieved good performance with limited spectral ranges. INDEX TERMS Cloud detection, cloud shadow, convolution neural networks, multiscale 3D-CNN.

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Section: A 3d-convolution Layermentioning

confidence: 99%

Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery

et al. 2020

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“…Classically, feature (such as LBP or SIFT) based [25,26] biometric verification is popular though it could be a bit timeconsuming. Recently new approaches such as deep neural networks [27][28][29][30] are taking over the field of biometric verification. Such biometric verification can be carried out on cloud servers, leading to a new topic called Biometrics-as-a-Service (BaaS).…”

Section: Privacy Issues In Biometric Blockchainmentioning

confidence: 99%

Biometric Blockchain: A Secure Solution for Intelligent Vehicle Data Sharing

Agbele

et al. 2020

Deep Biometrics

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The intelligent vehicle (IV) has become a promising technology that could revolutionize our life in smart cities sooner or later. However, it yet suffers from many security vulnerabilities. Traditional security methods are incapable to secure the IV data sharing against malicious attacks. Blockchain, as expected by both research and industry communities, has emerged as a good solution to address these issues. The major issues in IV data sharing are trust, data accuracy and reliability of data sharing in the communication channel. Blockchain technology, previously working for the cryptocurrency, has recently applied to build trust and reliability in peer-to-peer networks with similar topologies of IV data sharing. In this chapter, we present a new framework, namely biometric blockchain (BBC), for secure IV data sharing. In our new scheme, biometric information is exploited as a cue to record who is responsible in the data sharing activities, while the proposed BBC technology serves as the backbone of the IV data-sharing architecture. Hence, the proposed BBC technology provides a more reliable trust environment between the vehicles while personal identities are traceable in the proposed new scheme.

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“…More recently, DNNs have become the trend in face alignment. [33][34][35][36][37][38] For example, Feng et al proposed a Wing loss function for convolutional neural network (CNN)-based face alignment, which improves the performance of regression-based face alignment with CNNs significantly. 39 In this article, we use a modified regression visual geometry group (VGG) architecture to obtain fine-grained geometric facial features in the form of a set of 2D facial landmarks.…”

Section: Introductionmentioning

confidence: 99%

Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection

Mao

Delei

Chen

et al. 2020

International Journal of Advanced Robotic Systems

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Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.

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Integrated Deep Model for Face Detection and Landmark Localization From “In The Wild” Images

Cited by 35 publications

References 35 publications

Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery

Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery

Biometric Blockchain: A Secure Solution for Intelligent Vehicle Data Sharing

Deep aligned feature extraction for collaborative-representation-based face classification with group dictionary selection

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