Reaching high accuracy in handwritten character recognition is an essential challenge since it is widely used in many fields such as signature analysis and forgery detection. Recently, deep learning has demonstrated efficiency in this field. The problem with deep learning is that it uses a vast number of parameters that require a large dataset for training. To overcome this problem, an intelligent network is proposed in this study, based on the computational function of the dentate gyrus of the brain's hippocampus. The ability to separate patterns with high overlapping is a task that is referred to as the dentate gyrus. Handwritten character images have high overlapping due to various writers' styles, or even one writer's style under different conditions. Therefore, proposing a network based on the dentate gyrus' functional computation can be useful in this field. One of the prominent features of the proposed network is employing two excitation steps and two inhibition steps, augmenting the accuracy of recognizing handwritten characters. The proposed network was evaluated with six datasets of digits and characters from five languages. Experiments on all of the used datasets showed promising results. Moreover, a comparative and detailed analysis of the proposed network with other SOM-based and deep learning methods is provided. Experimental results show a significant boost in accuracy. While the character error rate (CER) was smaller than 1.85% for all the experiments, the smallest CER of 0.6% was achieved by the MNIST dataset. Moreover, in recognizing patterns with high noise, the proposed network showed satisfactory results.
Facial rejuvenation is the process of reversing the aging effects on the human face digitally. This paper generalizes a new approach for facial rejuvenation in adults image. Applications of facial rejuvenation are widespread. They include face recognition, education, entertainment, telecommunications, Psychology, criminal objects, Cosmetic arts and it can be used as an aid for medical cosmetics surgery and the reconstruction of the face. This paper proposes a novel facial rejuvenation modeling algorithm with two techniques. These techniques discuss the facial deformation based on the face anthropometrics theory and remove wrinkles based on what we called wrinkles inpainting. For example if we have been given a few different faces, we need to be able to compare the difference between the facial characteristics of the youth and the aged, then from there onwards, define a set of outlines which are going to be the basis of the simulation of Face Rejuvenation. The first is the geometric deformation details like skin texture, which differs between the aged and the youth. The second is anthropometrics data change. It was developed in the face anthropometrics measurement theory. Then together with warping technique we map the characteristics to any other particular persons' face in order to generate more expressive and convincing facial rejuvenation. The original contribution and advantage of this paper are that, the proposed methods are simple to implement, reliable, in which they required only one source image without needing to collect a lot of images and their computation are fast for interactive environment.
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