scholarly journals Pattern Recognition of Human Face With Photos Using KNN Algorithm

2021 ◽  
Vol 19 (1) ◽  
pp. 17
Author(s):  
Dedy Kurniadi ◽  
Andre Sugiyono ◽  
Linggar Alfithna Wardaya
Keyword(s):  
Pattern Recognition ◽  
Human Face

scholarly journals Improved Prototype Selection in Synergetic Pattern Recognition to Recognize Human Face Expressions

2013 ◽  
Vol 7 (4) ◽  
pp. 541-552
Author(s):  
Minchen Zhu ◽  
Weizhi Wang ◽  
Binghan Liu ◽  
Jingshan Huang
Keyword(s):  
Pattern Recognition ◽  
Prototype Selection ◽  
Human Face ◽  
Face Expressions

Introduction

2015 ◽  
pp. 3-7
Keyword(s):  
Pattern Recognition ◽  
Face Recognition ◽  
New Technologies ◽  
Similarity Measures ◽  
Growing Up ◽  
Human Face ◽  
Problem Statement ◽  
Video Images ◽  
Recognition Algorithms

Face recognition is a growing-up branch of pattern recognition in the context of image and vision. Conferences have arisen and brand new technologies have been coming to light providing more and more accurate recognition rates. But what is face recognition? The problem statement could be formulated this way: “Given still or video images of a scene, identify or verify one or more persons in the scene using a stored database of faces” [1]. Face recognition branch is core inasmuch the applications involving recognition algorithms for human face are aimed at different applications such as biometrics, authentication, identification of suspects. This chapter offers an overview of what are similarity and similarity measures.

scholarly journals Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2889
Author(s):  
Vassilis G. Kaburlasos ◽  
Chris Lytridis ◽  
Eleni Vrochidou ◽  
Christos Bazinas ◽  
George A. Papakostas ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Data Structures ◽  
Real World ◽  
Head Orientation ◽  
Human Face ◽  
Human Face Recognition ◽  
Face Representation ◽  
Tree Data ◽  
Tree Data Structure ◽  
Logic Reasoning

Social robots keep proliferating. A critical challenge remains their sensible interaction with humans, especially in real world applications. Hence, computing with real world semantics is instrumental. Recently, the Lattice Computing (LC) paradigm has been proposed with a capacity to compute with semantics represented by partial order in a mathematical lattice data domain. In the aforementioned context, this work proposes a parametric LC classifier, namely a Granule-based-Classifier (GbC), applicable in a mathematical lattice (T,⊑) of tree data structures, each of which represents a human face. A tree data structure here emerges from 68 facial landmarks (points) computed in a data preprocessing step by the OpenFace software. The proposed (tree) representation retains human anonymity during data processing. Extensive computational experiments regarding three different pattern recognition problems, namely (1) head orientation, (2) facial expressions, and (3) human face recognition, demonstrate GbC capacities, including good classification results, and a common human face representation in different pattern recognition problems, as well as data induced granular rules in (T,⊑) that allow for (a) explainable decision-making, (b) tunable generalization enabled also by formal logic/reasoning techniques, and (c) an inherent capacity for modular data fusion extensions. The potential of the proposed techniques is discussed.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Newborn's Preference for Faces

1996 ◽  
Vol 1 (3) ◽  
pp. 200-205 ◽  
Author(s):  
Carlo Umiltà ◽  
Francesca Simion ◽  
Eloisa Valenza
Keyword(s):  
Visual System ◽  
Structural Properties ◽  
Sensory Properties ◽  
Human Face ◽  
System Experiment ◽  
Face Experiment

Four experiments were aimed at elucidating some aspects of the preference for facelike patterns in newborns. Experiment 1 showed a preference for a stimulus whose components were located in the correct arrangement for a human face. Experiment 2 showed a preference for stimuli that had optimal sensory properties for the newborn visual system. Experiment 3 showed that babies directed their attention to a facelike pattern even when it was presented simultaneously with a non-facelike stimulus with optimal sensory properties. Experiment 4 showed the preference for facelike patterns in the temporal hemifield but not in the nasal hemifield. It was concluded that newborns' preference for facelike patterns reflects the activity of a subcortical system which is sensitive to the structural properties of the stimulus.

Review of Pattern Recognition by Self-Organizing Neural Networks.

1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Self Organizing

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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