The Influence of Activation Functions in Deep Learning Models Using Transfer Learning for Facial Age Prediction
Keywords:
Activation Function, facial age detection, Transfer Learning, Regression, Deep learning, Convolutional Neural Networks (CNNs).Abstract
Due to its superior performance, the convolutional neural network (CNN) has been extensively applied in image recognition. A facial age prediction technique based on the CNN model is suggested in this research, due to its many uses in the sports industry, access control and age verification systems. The activation function is at the center of the CNN model's complicated hierarchical structure since it has the nonlinear properties that give the deep neural network its accurate artificial intelligence. The ReLu function is one of the best common activation functions, however, it has flaws. It is likely to manifest as the phenomena of neuronal necrosis since the derivative of the ReLu function is always zero when the input value is negative. The impact of the activation function in the CNN model is also examined in this research to address the aforementioned issue. We look at both linear and nonlinear activation functions for facial age prediction using three facial datasets, namely UTK Facial, IMDB-WIKI and CASIA African Facial datasets. In facial age prediction tasks built on the Keras framework, we have investigated Linear and ReLu activation functions in the output layer on four CNN architectures namely VGG16, VGG19, ResNet50 and MobileNet. we compared each model using the two activation functions in the last output dense layer. The experimental results show that MobileNet using ReLu Performed the best with a Mean Absolute Error of 1.03 on the CASIA African Facial Dataset and VGG16 with a Mean Absolute Error of 1.75 using the linear activation function on the UTK Facial dataset, we also demonstrate that the convolutional neural network based on the modified activation function outperforms most state-of-the-art activation models in terms of performance.
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