Supervised and unsupervised face recognition method base on 3CCA

2012 ◽  
Author(s):  
Xiaoyuan Jing ◽  
Jie Sun ◽  
Yongfang Yao ◽  
Zaijuan Sui
Keyword(s):  
Face Recognition ◽  
Recognition Method ◽  
Method Base

Face recognition method based on DLPP-SIFT

2013 ◽  
Vol 32 (9) ◽  
pp. 2588-2591
Author(s):  
Zheng-yi LI ◽  
Gui-yu FENG ◽  
Long ZHAO
Keyword(s):  
Face Recognition ◽  
Recognition Method

scholarly journals Some Information Geometric Aspects of Cyber Security by Face Recognition

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 878
Author(s):  
C. T. J. Dodson ◽  
John Soldera ◽  
Jacob Scharcanski
Keyword(s):  
Face Recognition ◽  
Cyber Security ◽  
Probability Distributions ◽  
Geodesic Distance ◽  
Recognition Method ◽  
Frequency Distributions ◽  
Face Images ◽  
User Access ◽  
Multidimensional Methods ◽  
Joint Probabilities

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

Time-Lapse Data Oriented Infrared Face Recognition Method Using Block-PCA

2010 ◽  
Author(s):  
Yu Lu ◽  
Zhihua Xie ◽  
Zhijun Fang ◽  
Jucheng Yang ◽  
Feng Li ◽  
...  
Keyword(s):  
Face Recognition ◽  
Time Lapse ◽  
Recognition Method

A Fast Face Recognition Method Based on Fractal Coding

2017 ◽  
Vol 11 (7) ◽  
pp. 1221-1228 ◽  
Author(s):  
Zhijie Tang ◽  
Xiaocheng Wu ◽  
Xinnan Leng ◽  
Weiwei Chen
Keyword(s):  
Face Recognition ◽  
Recognition Method ◽  
Fractal Coding

Robust coarse-to-fine face recognition method

Optik ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4159-4165
Author(s):  
Qingxiang Feng ◽  
Qi Zhu ◽  
Lin-Lin Tang ◽  
Jeng-Shyang Pan
Keyword(s):  
Face Recognition ◽  
Recognition Method ◽  
Coarse To Fine
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