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