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This paper presents a face recognition algorithm that addresses two major challenges.
Here we propose a new 3D face recognition algorithm, entirely developed in Matlab®, whose framework totally comes from differential geometry.
In particular, the paper will focus on a recently proposed face recognition algorithm, where a sparse representation framework has been used to recover human identities from facial images that may be affected by illumination, occlusion, and facial disguise.
This study is concerned with a design of a face recognition algorithm realized based on feature extraction with the aid of the 2-directional 2-dimensional linear discriminant analysis referred to as (2D 2LDA.
'100% accurate' face recognition algorithm announced [The Register].
In this study we propose a face recognition algorithm based on a linear subspace projection.
Similar(31)
In the last decades, several three-dimensional face recognition algorithms have been thought, designed, and assessed.
"They can see everything: they've got face recognition algorithms looking through cameras on the streets, optical recognition cameras at bridges, tunnels and traffic lights.
It has been shown that accuracy in face and iris localization is crucial to face recognition algorithms.
These guys have applied the same machine learning techniques that have transformed face recognition algorithms to the problem of finding the next move in a game of Go.
Most of face recognition algorithms work fine when applied under controlled lighting conditions, proper distance and orientation of the subject from the camera and neutral expressions.
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