AIMC Topic: Biometric Identification

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EAC-Net: Deep Nets with Enhancing and Cropping for Facial Action Unit Detection.

IEEE transactions on pattern analysis and machine intelligence
In this paper, we propose a deep learning based approach for facial action unit (AU) detection by enhancing and cropping regions of interest of face images. The approach is implemented by adding two novel nets (a.k.a. layers): the enhancing layers an...

Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification.

International journal of neural systems
The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique "brain print", which is defined by the ...

Affect recognition from facial movements and body gestures by hierarchical deep spatio-temporal features and fusion strategy.

Neural networks : the official journal of the International Neural Network Society
Affect presentation is periodic and multi-modal, such as through facial movements, body gestures, and so on. Studies have shown that temporal selection and multi-modal combinations may benefit affect recognition. In this article, we therefore propose...

Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video.

IEEE transactions on pattern analysis and machine intelligence
Riemannian manifolds have been widely employed for video representations in visual classification tasks including video-based face recognition. The success mainly derives from learning a discriminant Riemannian metric which encodes the non-linear geo...

Sharable and Individual Multi-View Metric Learning.

IEEE transactions on pattern analysis and machine intelligence
This paper presents a sharable and individual multi-view metric learning (MvML) approach for visual recognition. Unlike conventional metric leaning methods which learn a distance metric on either a single type of feature representation or a concatena...

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition.

Computational intelligence and neuroscience
A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its e...

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...