Face recognition performance analysis: Cohort classifiers

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
Marcel Van der Made ◽  
Rauf Mahmudlu ◽  
Halim Munawar ◽  
Moriz Neikes ◽  
Niels Samwel
Keyword(s):  
Face Recognition ◽  
Performance Analysis ◽  
Recognition Performance

scholarly journals Modelling of Facial Images for Analysis of Recognition System

2021 ◽  
Vol 2107 (1) ◽  
pp. 012041
Author(s):  
Assyakirin M H ◽  
Shafriza Nisha B ◽  
Haniza Y ◽  
Fathinul Syahir A S ◽  
Muhammad Juhairi A S
Keyword(s):  
Face Recognition ◽  
Performance Analysis ◽  
Facial Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Background Intensity ◽  
Face Recognition System ◽  
Recognition Systems ◽  
Human Faces ◽  
Facial Images

Abstract Face recognition is categorized as a biometric technology that employs the use of computer ability in image processing to detect and recognize human faces. Face recognition system has numerous applications for many purposes such as for access control, law enforcement and surveillance thus this system is dominant in present technology. Generally, face recognition system become more advance in term of the accuracy and implementation. However, there are a few parameters that effects the accuracy of recognition system for examples, the pose invariant, illumination effect, size of image and noise tolerance. Even though there are a number of systems were already available in the literature, the complete understanding of their performances are relatively limited. This is due to many systems focused on a narrow application band – therefore, a comprehensive analysis are needed in order to understand their performances leading to establishing the conditions for successful face recognition system. In this paper we developed a synthetic model to represent facial images to be used as a platform for performance analysis of facial recognition systems. The model includes 5 face types with the ability to vary all parameters that are affecting recognition performance – measurement noise, face size and face-background intensity differences. The model is important as it provide an avenue for performance analysis of facial recognition systems.

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.

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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