scholarly journals Hierarchical Image Matching for Pose-invariant Face Recognition

2009 ◽  
Author(s):  
Shervin Rahimzadeh Arashloo ◽  
Josef Kittler
Keyword(s):  
Face Recognition ◽  
Image Matching

Morphable Displacement Field Based Image Matching for Face Recognition across Pose

2012 ◽  
pp. 102-115 ◽  
Author(s):  
Shaoxin Li ◽  
Xin Liu ◽  
Xiujuan Chai ◽  
Haihong Zhang ◽  
Shihong Lao ◽  
...  
Keyword(s):  
Face Recognition ◽  
Displacement Field ◽  
Image Matching

scholarly journals Face Identification on Login Security Using Algorithm Combination of Viola-Jones and Cosine Similarity

2020 ◽  
Vol 4 (1) ◽  
pp. 203-211
Author(s):  
FADHILLAH AZMI ◽  
Amir Saleh ◽  
N P Dharshinni
Keyword(s):  
Face Recognition ◽  
Data Security ◽  
Image Matching ◽  
Cosine Similarity ◽  
Facial Features ◽  
Gray Level ◽  
Image Detection ◽  
Texture Characteristics ◽  
Occurrence Matrix ◽  
Different Characteristics

Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images.Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.

scholarly journals An affine view and illumination invariant iterative image matching approach for face recognition

2018 ◽  
Vol 7 (2.8) ◽  
pp. 42
Author(s):  
D Rajasekhar ◽  
T Jayachandra Prasad ◽  
K Soundararajan
Keyword(s):  
Face Recognition ◽  
Image Matching ◽  
Feature Detection ◽  
Scale Invariant ◽  
Data Set ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Affine Transforms

Feature detection and image matching constitutes two primary tasks in photogrammetric and have multiple applications in a number of fields. One such application is face recognition. The critical nature of this application demands that image matching algorithm used in recognition of features in facial recognition to be robust and fast. The proposed method uses affine transforms to recognize the descriptors and classified by means of Bayes theorem. This paper demonstrates the suitability of the proposed image matching algorithm for use in face recognition appli-cations. Yale facial data set is used in the validation and the results are compared with SIFT (Scale Invariant Feature Transform) based face recognition approach.

Research and Improvement on HMM-Based Face Recognition

2014 ◽  
Vol 490-491 ◽  
pp. 1338-1341 ◽  
Author(s):  
Yan Fen Cheng ◽  
Hao Tang ◽  
Xian Qiao Chen
Keyword(s):  
Face Recognition ◽  
Image Matching ◽  
Viterbi Algorithm ◽  
Optimal Parameters ◽  
Discrete Cosine Transformation ◽  
Recognition Time ◽  
Cosine Transformation ◽  
Model Efficiency ◽  
Recognition Efficiency ◽  
Model Training

This paper is concerned with face recognition using the hidden Markov model with 2D-discrete cosine transformation observations. The first part of the paper mainly discusses the influence of sampling parameter selection on model training and recognition efficiency and proposes method to increases the model efficiency through selecting optimal combinations of input parameters. In the second part of the paper, we choose the optimal parameters as input data standard for image matching and extend Viterbi algorithm by setting thresholds, the recognition time is reduced by 12.4% on average.

Compositional Analysis of III-V Interface Lattice Images

1980 ◽  
Vol 38 ◽  
pp. 318-319
Author(s):  
A. Olsen ◽  
J.C.H. Spence ◽  
P. Petroff
Keyword(s):  
Crystal Structure ◽  
Image Matching ◽  
Compositional Analysis ◽  
Depth Of Field ◽  
Limit Set ◽  
High Contrast ◽  
Resolution Limit ◽  
Fourier Image ◽  
Lattice Images ◽  
True Structure

Since the point resolution of the JEOL 200CX electron microscope is up = 2.6Å it is not possible to obtain a true structure image of any of the III-V or elemental semiconductors with this machine. Since the information resolution limit set by electronic instability (1) u0 = (2/πλΔ)½ = 1.4Å for Δ = 50Å, it is however possible to obtain, by choice of focus and thickness, clear lattice images both resembling (see figure 2(b)), and not resembling, the true crystal structure (see (2) for an example of a Fourier image which is structurally incorrect). The crucial difficulty in using the information between Up and u0 is the fractional accuracy with which Af and Cs must be determined, and these accuracies Δff/4Δf = (2λu2Δf)-1 and ΔCS/CS = (λ3u4Cs)-1 (for a π/4 phase change, Δff the Fourier image period) are strongly dependent on spatial frequency u. Note that ΔCs(up)/Cs ≈ 10%, independent of CS and λ. Note also that the number n of identical high contrast spurious Fourier images within the depth of field Δz = (αu)-1 (α beam divergence) decreases with increasing high voltage, since n = 2Δz/Δff = θ/α = λu/α (θ the scattering angle). Thus image matching becomes easier in semiconductors at higher voltage because there are fewer high contrast identical images in any focal series.

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