Convolutional neural networks for attribute-based active authentication on mobile devices

Samangouei, Pouya; Chellappa, Rama

doi:10.1109/btas.2016.7791163

Cited by 33 publications

(17 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of deep learning, attribute-enhanced face recognition does not gain too much attention. One related work [33] is to exploit CNN based attribute features for authentication on mobile devices, and the facial attributes are trained by a multi-task, partly based Deep Convolutional Neural Network architecture. Hu et.al [11] systematically study the problem of how to fuse face recognition features and facial attribute features to enhance face recognition performance.…”

Section: Face Recognition With Attributesmentioning

confidence: 99%

Robust RGB-D Face Recognition Using Attribute-Aware Loss

Jiang

Zhang

Deng

2020

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

Existing convolutional neural network (CNN) based face recognition algorithms typically learn a discriminative feature mapping, using a loss function that enforces separation of features from different classes and/or aggregation of features within the same class. However, they may suffer from bias in the training data such as uneven sampling density, because they optimize the adjacency relationship of the learned features without considering the proximity of the underlying faces. Moreover, since they only use facial images for training, the learned feature mapping may not correctly indicate the relationship of other attributes such as gender and ethnicity, which can be important for some face recognition applications. In this paper, we propose a new CNN-based face recognition approach that incorporates such attributes into the training process. Using an attribute-aware loss function that regularizes the feature mapping using attribute proximity, our approach learns more discriminative features that are correlated with the attributes. We train our face recognition model on a large-scale RGB-D data set with over 100K identities captured under real application conditions. By comparing our approach with other methods on a variety of experiments, we demonstrate that depth channel and attribute-aware loss greatly improve the accuracy and robustness of face recognition.

show abstract

Section: Face Recognition With Attributesmentioning

confidence: 99%

Robust RGB-D Face Recognition Using Attribute-Aware Loss

Jiang

Zhang

Deng

2020

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…There has been little work on attribute-enhanced face recognition in the context of deep learning. One of the few exploits CNNbased attribute features for authentication on mobile devices [31]. Local facial patches are fed into carefully designed CNNs to predict different attributes.…”

Section: Related Workmentioning

confidence: 99%

Attribute-Enhanced Face Recognition with Neural Tensor Fusion Networks

Hu¹,

Yang²,

Yang³

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

View full text Add to dashboard Cite

“…To deal with this issue, Active Authentication (AA) systems were introduced in which users are continuously monitored after the initial access to the mobile device [3]. Various methods have been proposed for AA including touch gesture-based [4], [5], [6], [7], gait patternbased [8] and face-based [9], [10], [11], [12], [13] systems. In particular, face-based AA systems have gained a lot of attraction in recent years.…”

Section: Introductionmentioning

confidence: 99%

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Oza

Patel

2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

View full text Add to dashboard Cite

Active authentication refers to the process in which users are unobtrusively monitored and authenticated continuously throughout their interactions with mobile devices. Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users. Normally, the enrolled user is considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise and an autoencoder are used to model the pseudo-negative class and to regularize the network to learn meaningful feature representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. Effectiveness of the proposed framework is demonstrated using three publically available face-based active authentication datasets and it is shown that the proposed method achieves superior performance compared to the traditional one class classification methods. The source code is available at : github.com/otkupjnoz/oc-acnn.

show abstract

Convolutional neural networks for attribute-based active authentication on mobile devices

Cited by 33 publications

References 34 publications

Robust RGB-D Face Recognition Using Attribute-Aware Loss

Robust RGB-D Face Recognition Using Attribute-Aware Loss

Attribute-Enhanced Face Recognition with Neural Tensor Fusion Networks

Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier

Contact Info

Product

Resources

About