An Algorithm for Selecting Face Features Using Deep Learning Techniques Based on Autoencoders

In recent years, deep learning as a part of the artificial intelligence theory has formed the basis for many advanced developments, such as drone, voice and image recognition technologies, and etc. The concept of deep learning is closely related to artificial neural networks. On the other hand deep learning techniques work with unmarked data. For this reason, deep learning algorithms show their effectiveness in face recognition. But there are a number of difficulties related to implementation of deep learning algorithms. Deep learning requires a large amount of unmarked data and long training. In this presentation a new algorithm for automatic selection of face features using deep learning techniques based on autoencoders in combination with customized loss functions to provide high informativeness with low within-class and high between-class variance is proposed. The multilayer networks of feed forward type are used. The extracted features are used for face classification. The performance of the proposed system for processing, analyzing and classifying persons from face images is compared with that of state-of-art algorithms.