An algorithm for selecting face features using deep learning techniques based on autoencoders

Sergey Leonov; Alexander Vasilyev; Artyom Makovetskii; Vladislav Kuznetsov; J. Diaz-Escobar

doi:10.1117/12.2321068

17 September 2018 An algorithm for selecting face features using deep learning techniques based on autoencoders

Sergey Leonov, Alexander Vasilyev, Artyom Makovetskii, Vladislav Kuznetsov, J. Diaz-Escobar

Proceedings Volume 10752, Applications of Digital Image Processing XLI; 107522M (2018) https://doi.org/10.1117/12.2321068
Event: SPIE Optical Engineering + Applications, 2018, San Diego, California, United States

Abstract

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 withinclass 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.

Citation Download Citation

Sergey Leonov, Alexander Vasilyev, Artyom Makovetskii, Vladislav Kuznetsov, and J. Diaz-Escobar "An algorithm for selecting face features using deep learning techniques based on autoencoders", Proc. SPIE 10752, Applications of Digital Image Processing XLI, 107522M (17 September 2018); https://doi.org/10.1117/12.2321068

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available