Age Invariant Face Recognition Model Based on Convolution Neural Network (CNN)

Abstract

Building an intelligent system similar to the human perception system in face recognition is still an active area of research, despite the advancements in technologies and face recognition research carried out when age changes. Deep learning algorithms have outperformed conventional methods in with regard to accuracy and effectiveness of recognition a variety of difficulties, including position, expression, lighting, and aging. But aging is one of the problems that affects the face the most, as it plays a significant role which directly affects facial features, so we notice some people who are very difficult to distinguish and may not be known at all because of the strong change in their features. As a result, we researched deep learning techniques generally and the convolutional neural network (CNN) specifically. This strategy is employed by a number of significant stages: The first side, includes preparing the dataset related to the subject of the study, Isolate the data between training, validation and testing. As for the second part of the work, data preprocessing, such a data augmentation, Normalization, Face detection, and resizing. After then, begin a features extraction operation by the convolution neural network (CNN) that is suggested. After all that, the classification stage begins, which was done by using the (SoftMax) function, because we have approximately (570) classes. In the testing phase, we perform the task of checking the two images entered whether they belong to the same person or not. In this paper, adopted the (Age) and (FG-Net) datasets, Finally, the verification accuracy rate for the proposed system reached 98.7 % on the (Age) dataset, and reached 99.4 % on the (FG-Net) dataset.