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Biometric Identification

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Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach.

IEEE transactions on pattern analysis and machine intelligence
Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute corr...

Two-Stream Transformer Networks for Video-Based Face Alignment.

IEEE transactions on pattern analysis and machine intelligence
In this paper, we propose a two-stream transformer networks (TSTN) approach for video-based face alignment. Unlike conventional image-based face alignment approaches which cannot explicitly model the temporal dependency in videos and motivated by the...

F-norm distance metric based robust 2DPCA and face recognition.

Neural networks : the official journal of the International Neural Network Society
Two-dimensional principal component analysis (2DPCA) employs squared F-norm as the distance metric for dimensionality reduction. It is commonly known that squared F-norm is sensitive to the presence of outliers. To address this problem, we use F-norm...

A novel deep learning algorithm for incomplete face recognition: Low-rank-recovery network.

Neural networks : the official journal of the International Neural Network Society
There have been a lot of methods to address the recognition of complete face images. However, in real applications, the images to be recognized are usually incomplete, and it is more difficult to realize such a recognition. In this paper, a novel con...

Learning from Ambiguously Labeled Face Images.

IEEE transactions on pattern analysis and machine intelligence
Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We p...

Discriminative Deep Metric Learning for Face and Kinship Verification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
This paper presents a new discriminative deep metric learning (DDML) method for face and kinship verification in wild conditions. While metric learning has achieved reasonably good performance in face and kinship verification, most existing metric le...

Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we propose a simultaneous feature and dictionary learning (SFDL) method for image set-based face recognition, where each training and testing example contains a set of face images, which were captured from different variations of pose,...

Trunk-Branch Ensemble Convolutional Neural Networks for Video-Based Face Recognition.

IEEE transactions on pattern analysis and machine intelligence
Human faces in surveillance videos often suffer from severe image blur, dramatic pose variations, and occlusion. In this paper, we propose a comprehensive framework based on Convolutional Neural Networks (CNN) to overcome challenges in video-based fa...

Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance variables, both linear (i.e., add...

A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems.

Neural networks : the official journal of the International Neural Network Society
Over the past few decades, the design of an intelligent Intrusion Detection System (IDS) remains an open challenge to the research community. Continuous efforts by the researchers have resulted in the development of several learning models based on A...