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Facial Age Estimation Using Convolution Neural Networks

Age-related analysis has been a concern in the current years because many implementations have a great significance. The techniques of facial age prediction and classification are commonly used in the recent years for vitality applications but these techniques are time-consuming. The deep algorithms demonstrated superior efficiency compared to other approaches in order to solve the problem of age estimation. Herein, an age classification model is proposed using the mechanisms of deep age estimation in this article. This work introduces an age recognition model that helps to classify a person's image into a suitable aging group. The proposed model achieved better results in the prediction process with an accuracy reached 85.7% using InceptionV4. The main difference between this work and the relevant related works is that this work focuses on highlighting the performance of four pre-trained models three of them have different architectures; ResNet50, ResNet101, Sequeeze1_0, and InceptionV4. In deep age evaluation schemes, we look at previous study initiatives and current common datasets.