Multi sensor, multi algorithm based face recognition & performance evaluation

2016 ◽  
Author(s):  
Siddharth B. Dabhade ◽  
Nagsen S. Bansod ◽  
Yogesh S. Rode ◽  
M. M. Kazi ◽  
K. V. Kale
Keyword(s):  
Performance Evaluation ◽  
Face Recognition ◽  
Recognition Performance

A New Approach for Face Recognition Performance Evaluation for Robot Using LED Monitor

2014 ◽  
Vol 548-549 ◽  
pp. 939-942 ◽  
Author(s):  
Mi Young Cho ◽  
Young Sook Jeong ◽  
Byung Tae Chun
Keyword(s):  
Performance Evaluation ◽  
Face Recognition ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Test Methods ◽  
Human Robot Interaction ◽  
Point Of View ◽  
Service Robots ◽  
Face Detection And Recognition ◽  
Performance Evaluation Method

With the increasing of service robots, human-robot interaction for natural communication between user and robot is becoming more and more important. Especially, face recognition is a key issue of HRI. Even though robots mainly use face detection and recognition to provide various services, it is still difficult to guarantee of performance due to insufficient test methods in point of view robot. So, we propose a new performance evaluation method for robot using LED monitor.

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.

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

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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