A Comparative Analysis of Various Deep Learning Models for Facial Recognition

The main idea behind facial recognition is to identify human faces in images as well as videos. Multiple techniques have been developed till date to improve the performance of facial recognition systems. Earlier methods were not competent of capturing prominent facial information due to pose, illumination, occlusion, resolution and skin color effects. A new emerging technique known as deep learning is showing impressive improvement in performance metrics for facial identification. With recent developments in deep learning techniques, facial detection systems can learn data by themselves and can extract useful features. It has been shown to exhibit promising recognition results for diverse real time applications. In this paper, we have examined deep convolutional neural networks and discussed various models related to deep learning such as VGGNet, GoogleNet, SqueezeNet, AlexNet and ResNet and have compared these with respect to various evaluation metrics such as error rate, prediction time and accuracy percentage.