Coarse to Fine Hierarchical Tracking System for Face Recognition

2006 ◽  
Author(s):  
T. Funahashi ◽  
T. Fujiwara ◽  
H. Koshimizu
Keyword(s):  
Face Recognition ◽  
Tracking System ◽  
Coarse To Fine

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

Coarse-to-Fine(r) Automatic Familiar Face Recognition in the Human Brain

2021 ◽  
Author(s):  
Xiaoqian Yan ◽  
Valérie Goffaux ◽  
Bruno Rossion
Keyword(s):  
Face Recognition ◽  
Human Brain ◽  
Spatial Resolution ◽  
Predictive Coding ◽  
Head Width ◽  
Familiar Face ◽  
Visual Inputs ◽  
Electroencephalogram Eeg ◽  
Coarse To Fine ◽  
Unfamiliar Faces

Abstract At what level of spatial resolution can the human brain recognize a familiar face in a crowd of strangers? Does it depend on whether one approaches or rather moves back from the crowd? To answer these questions, 16 observers viewed different unsegmented images of unfamiliar faces alternating at 6 Hz, with spatial frequency (SF) content progressively increasing (i.e., coarse-to-fine) or decreasing (fine-to-coarse) in different sequences. Variable natural images of celebrity faces every sixth stimulus generated an objective neural index of single-glanced automatic familiar face recognition (FFR) at 1 Hz in participants’ electroencephalogram (EEG). For blurry images increasing in spatial resolution, the neural FFR response over occipitotemporal regions emerged abruptly with additional cues at about 6.3–8.7 cycles/head width, immediately reaching amplitude saturation. When the same images progressively decreased in resolution, the FFR response disappeared already below 12 cycles/head width, thus providing no support for a predictive coding hypothesis. Overall, these observations indicate that rapid automatic recognition of heterogenous natural views of familiar faces is achieved from coarser visual inputs than generally thought, and support a coarse-to-fine FFR dynamics in the human brain.

scholarly journals DYNAMIC FACE RECOGNITION AND TRACKING SYSTEM USING MACHINE LEARNING IN MATLAB AND BIGDATA

2017 ◽  
Vol 10 (4) ◽  
pp. 163-173
Author(s):  
P.J Leo Evenss ◽  
Jennings Mcenroe .S ◽  
A.Prabhu Chakkaravarthy
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Tracking System
Sign in / Sign up

Export Citation Format

Share Document