On the Recognition Performance of BioHashing on state-of-the-art Face Recognition models

2021 ◽  
Author(s):  
Hatef Otroshi Shahreza ◽  
Vedrana Krivokuca Hahn ◽  
Sebastien Marcel
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Performance

3D Face Recognition in the Presence of Partial Data

2014 ◽  
pp. 70-97
Author(s):  
Dat Chu ◽  
Shishir Shah ◽  
Ioannis A. Kakadiaris
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Performance ◽  
Recognition Task ◽  
Training Data ◽  
3D Face Recognition ◽  
Coupling Term ◽  
3D Face ◽  
The Face ◽  
The Right

Performing face recognition under extreme poses and lighting conditions remains a challenging task for current state-of-the-art biometric algorithms. The recognition task is even more challenging when there is insufficient training data available in the gallery, or when the gallery dataset originates from one side of the face while the probe dataset originates from the other. The authors present a new method for computing the distance between two biometric signatures acquired under such challenging conditions. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost, and the semi-coupling cost. The use of a semi-coupling term allows the method to handle partial 3D face meshes where, for example, only the left side of the face is available for gallery and the right side of the face is available for probe. The method also extends to 2D signatures under varying poses and lighting changes by using 3D signatures as a coupling term. The experiments show that this method can improve recognition performance of existing state-of-the-art wavelet signatures used in 3D face recognition and provide excellent recognition results in the 3D-2D face recognition application.

Advances in Moving Face Recognition

2011 ◽  
pp. 167-178
Author(s):  
Hui Fang ◽  
Nicolas Costen ◽  
Phil Grant ◽  
Min Chen
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Performance ◽  
Experimental Tests ◽  
Motion Information ◽  
Subspace Analysis ◽  
Face Images ◽  
Motion Features ◽  
Art Techniques

This chapter describes the approaches to extracting features via the motion subspace for improving face recognition from moving face sequences. Although the identity subspace analysis has achieved reasonable recognition performance in static face images, more recently there has been an interest in motion-based face recognition. This chapter reviews several state-of-the-art techniques to exploit the motion information for recognition and investigates the permuted distinctive motion similarity in the motion subspace. The motion features extracted from the motion subspaces are used to test the performance based on a verification experimental framework. Through experimental tests, the results show that the correlations between motion eigen-patterns significantly improve the performance of recognition.

scholarly journals FaceFilter: Face Identification with Deep Learning and Filter Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Mohammed Alghaili ◽  
Zhiyong Li ◽  
Hamdi A. R. Ali
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition Performance ◽  
Face Identification ◽  
Original Image ◽  
Convolutional Network ◽  
The Face ◽  
In The Wild ◽  
Minimum Number ◽  
Different Levels

Although significant advances have been made recently in the field of face recognition, these have some limitations, especially when faces are in different poses or have different levels of illumination, or when the face is blurred. In this study, we present a system that can directly identify an individual under all conditions by extracting the most important features and using them to identify a person. Our method uses a deep convolutional network that is trained to extract the most important features. A filter is then used to select the most significant of these features by finding features greater than zero, storing their indices, and comparing the features of other identities with the same indices as the original image. Finally, the selected features of each identity in the dataset are subtracted from features of the original image to find the minimum number that refers to that identity. This method gives good results, as we only extract the most important features using the filter to recognize the face in different poses. We achieve state-of-the-art face recognition performance using only half of the 128 bytes per face. The system has an accuracy of 99.7% on the Labeled Faces in the Wild dataset and 94.02% on YouTube Faces DB.

Face Perception in Face Transplant Patients

2019 ◽  
Vol 35 (05) ◽  
pp. 525-533
Author(s):  
Evrim Gülbetekin ◽  
Seda Bayraktar ◽  
Özlenen Özkan ◽  
Hilmi Uysal ◽  
Ömer Özkan
Keyword(s):  
Face Recognition ◽  
Object Recognition ◽  
Face Processing ◽  
Recognition Performance ◽  
Discrimination Performance ◽  
Object Discrimination ◽  
Face Transplantation ◽  
Transplant Patients ◽  
Facial Surgery ◽  
The Face

AbstractThe authors tested face discrimination, face recognition, object discrimination, and object recognition in two face transplantation patients (FTPs) who had facial injury since infancy, a patient who had a facial surgery due to a recent wound, and two control subjects. In Experiment 1, the authors showed them original faces and morphed forms of those faces and asked them to rate the similarity between the two. In Experiment 2, they showed old, new, and implicit faces and asked whether they recognized them or not. In Experiment 3, they showed them original objects and morphed forms of those objects and asked them to rate the similarity between the two. In Experiment 4, they showed old, new, and implicit objects and asked whether they recognized them or not. Object discrimination and object recognition performance did not differ between the FTPs and the controls. However, the face discrimination performance of FTP2 and face recognition performance of the FTP1 were poorer than that of the controls were. Therefore, the authors concluded that the structure of the face might affect face processing.

scholarly journals Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ying Li ◽  
Hang Sun ◽  
Shiyao Feng ◽  
Qi Zhang ◽  
Siyu Han ◽  
...  
Keyword(s):  
Protein Interactions ◽  
State Of The Art ◽  
Recognition Performance ◽  
Feature Learning ◽  
Biological Processes ◽  
Multimodal Features ◽  
Learning Architectures ◽  
Motif Information ◽  
Experimental Comparisons ◽  
Better Than

Abstract Background Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA–protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs. Results We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement. Conclusions This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver (http://csbg-jlu.site/lpc/predict) is developed to be convenient for users.

Virtual samples based robust block-diagonal dictionary learning for face recognition

2021 ◽  
Vol 25 (5) ◽  
pp. 1273-1290
Author(s):  
Shuangxi Wang ◽  
Hongwei Ge ◽  
Jinlong Yang ◽  
Shuzhi Su
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Recognition Performance ◽  
Small Sample Size ◽  
Small Sample ◽  
Low Rank ◽  
Negative Effects ◽  
Linear Classifier ◽  
Virtual Samples ◽  
Block Diagonal

It is an open question to learn an over-complete dictionary from a limited number of face samples, and the inherent attributes of the samples are underutilized. Besides, the recognition performance may be adversely affected by the noise (and outliers), and the strict binary label based linear classifier is not appropriate for face recognition. To solve above problems, we propose a virtual samples based robust block-diagonal dictionary learning for face recognition. In the proposed model, the original samples and virtual samples are combined to solve the small sample size problem, and both the structure constraint and the low rank constraint are exploited to preserve the intrinsic attributes of the samples. In addition, the fidelity term can effectively reduce negative effects of noise (and outliers), and the ε-dragging is utilized to promote the performance of the linear classifier. Finally, extensive experiments are conducted in comparison with many state-of-the-art methods on benchmark face datasets, and experimental results demonstrate the efficacy of the proposed method.

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