Due to the development in the digital image
processing, its wide use in many applications such as medical,
security, and others, the need for more accurate techniques that
are reliable, fast and robust is vehemently demanded. Face
recognition technology is very important for the security that
provides intelligence services. Recently, a new trend has emerged
to raise the efficiency of the facial recognition systems by using
neural networks. Furthermore, using Convolution Neural
Networks (CNN) with a huge number of images in databases has
made the deep learning technique very beneficial. The use of deep
learning networks supports to learn much more complicated and
high-level abstracted features automatically not handcrafted to
improve recognition accuracy. Our objective is to enhance 2D
face recognition accuracies based on convolution neural network
which consists of 15 layers to learn discriminative representation.
We use CNN training on differently aligned face images and use
stochastic gradient descent algorithm to train the feature
extractor and the classifier, which can extract the facial features
and classify them automatically. The experiments on the Face96
database show that our proposed method achieves 99.67%
accuracy |
