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Face Recognition using Convolutional Neural Network
and Simple Logistic Classifier
Hurieh KhalajzadehMohammad Mansouri
Mohammad Teshnehlab
Table of ContentsConvolutional Neural NetworksProposed CNN structure for face recognitionLogistic ClassifierResult of CNN with winner takes all
mechanismComparison of using different algorithms for
classifyingResults of proposed methodConclusion
Convolutional Neural NetworksIntroduced by Yann LeCun and Yoshua
Bengio in 1995Feed-forward networks with the ability of
extracting topological properties from the input image
Invariance to distortions and simple geometric transformations like translation, scaling, rotation and squeezing
Alternate between convolution layers and subsampling layers
LeNet5 Architecture
CNN structure used for feature extraction
Interconnection of first subsampling layer with the second convolutional layer
Learning Rate
0 100 200 300 400 5000
0.02
0.04
0.06
0.08
0.1
Epoch
0 100 200 300 400 5000
0.02
0.04
0.06
0.08
0.1
Epoch
Yale face database
64×64[-1, 1]
logistic function
-5 -4 -3 -2 -1 0 1 2 3 4 50
0.5
1
X
Y
Y = 1/(1 + exp(-X))
Recognition accuracy, training time and number of parameters
0 50 100 150 200 250 300 350 400 450 5000
20
40
60
80
100
Epoch
Acc
urac
y(%
)
Test Accuracy
Train Accuracy
Comparison of different algorithms
X. Shu et al. / Pattern Recognition 45 (2012) 1892-1898
Classification accuracy
Classification time
ConclusionConvolutional neural networks and simple
logistic regression method are investigated with results on Yale face dataset
Method benefit from all CNN advantages such as feature extracting and robustness to distortions
Simple logistic regression which is a discriminative classifier is more efficient when the normality assumptions are satisfied.
Results show the highest classification accuracy and lowest classification time in compare with other machine learning algorithms