Abstract
One of the complex procedures which affect man’s face shape and texture is facial aging. These changes tend to deteriorate the efficacy of systems that automatically verify faces. It seems that CNN (also known as Convolutional Neural Networks) are thought to be one of the most common deep learning approaches where multiple layers are trained robustly while maintaining the minimum number of learned parameters to improve system performance. In this paper, a deeper model of convolutional neural network is fitted with Histogram of Oriented Gradients (HOG) descriptor to handle feature extraction and classification of two face images with the age gap is proposed. Furthermore, the model has been trained and tested in the MORPH and FG-NET datasets. Experiments on FG-NET achieve a state of the arts accuracy (reaching 100%) while results on MORPH dataset have significant improvements in accuracy of 99.85%.
Highlights
As age proceeds, face appearance is affected dramatically which is a phenomenon [1]
The database was classified into a couple of categories: in the first, 80% of the data was picked arbitrary to train the Convolutional Neural Network (CNN) network, whereas the other 20% was utilized to examine it
Histogram of Oriented Gradients (HOG) descriptor with deep convolutional neural network reached a maximum accuracy of 100% that is the same when combining both Local Binary Pattern (LBP) and HOG within the same CNN
Summary
Face appearance is affected dramatically which is a phenomenon [1]. Despite the fact that age effects on face appearance have been studied for a while, novice work to reveal faces during age progress has been done. One of the most emergent issues is how to identify an invariant facial feature. The basic problem of this research is how to develop a scheme that represents and matches facial features and that is flexible to deal with different face aging changes. What is a suitable algorithm to extract invariant features is the one that improves performance throughout the system by boosting its accuracy. A suitable algorithm stands out when compared with other systems that identify images of people as they age. The main issue, is how to build model architecture to improve system performance. In the literature, both deep learning-based approaches and Convolutional Neural Network (CNN) has been used for face verification. The method has been assessed in accordance with the LAG (Large Age Gap) database and proved to function better than other contemporary state-of-the-art systems
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