Local binary patterns preprocessing for face identification/verification using the VanderLugt correlator

2014 ◽  
Author(s):  
Thibault Napoléon ◽  
Ayman Alfalou
Keyword(s):  
Local Binary Patterns ◽  
Face Identification ◽  
Vanderlugt Correlator

scholarly journals Face Identification Using LBP-Based Improved Directional Wavelet Transform

2020 ◽  
Author(s):  
Mohd. Abdul Muqeet ◽  
Qazi Mateenuddin Hameeduddin
Keyword(s):  
Computer Vision ◽  
Wavelet Transform ◽  
Facial Expression ◽  
Local Binary Patterns ◽  
Face Identification ◽  
Illumination Variation ◽  
Local Descriptor ◽  
Identification Methods ◽  
Face Images ◽  
Directional Wavelet

Face identification is the most active area of research in computer vision and biometric authentication. Various face identification methods are developed over the time, still, numerous facial appearances are needed to cope with such as facial expression, pose, and illumination variation. Moreover, faces captured in unrestrained situations also impose immense concern in designing effective face identification methods. It is desirable to extract robust local descriptive features to effectively characterize such facial variations both in unrestrained and restrained situations. This chapter discusses such a face identification method that incorporate a popular local descriptor such as local binary patterns (LBP) based on the improved directional wavelet transform (IDW) method to extract facial features. This designed method is applied to complex face databases such as CASIA-WebFace and LFW which consists of a large number of face images collected under an unrestrained environment with extreme facial variations in expression, pose, and illumination. Experiments and comparison with various methods which include not only the local descriptive methods but also local descriptive-based multiresolution analysis (MRA) based methods demonstrate the efficacy of the LBP-based IDW method.

ALGORITHMS FOR IMAGE PRE-PROCESSING IN THE FACE IDENTIFICATION SYSTEM IN THE VIDEO STREAM

2019 ◽  
Vol 4 (91) ◽  
pp. 21-29 ◽  
Author(s):  
Yaroslav Trofimenko ◽  
Lyudmila Vinogradova ◽  
Evgeniy Ershov
Keyword(s):  
Video Stream ◽  
Face Identification ◽  
Identification System ◽  
The Face

Local Binary Patterns with ENI features for Images classification and analysis

2011 ◽  
Vol 2 (1) ◽  
pp. 45-50
Author(s):  
E. Suresh Babu ◽  
S. Salma ◽  
A. Reshma ◽  
C. Nagaraju
Keyword(s):  
Local Binary Patterns

scholarly journals Deformable Models for Segmentation Based on Local Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jimena Olveres ◽  
Erik Carbajal-Degante ◽  
Boris Escalante-Ramírez ◽  
Enrique Vallejo ◽  
Carla María García-Moreno
Keyword(s):  
Local Binary Patterns ◽  
Distance Measures ◽  
Local Analysis ◽  
Hermite Transform ◽  
Active Shape Models ◽  
Shape Models ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Image Modality ◽  
New Algorithms

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

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