Identification of individuals based on their images has become increasingly important, to give access to certain users and to block the unknowns, in order to maintain security. A new access control approach based on facial recognition using the pix2pix generative classifier with a new decision making method was tested using the Olivetti Research Laboratory database. This approach requires a simple comparison between the generated data and the reference database using a predefined threshold. For the testing, a different number of individuals were excluded from the training database and the network was generally able to reject unknown individuals, recognize and identify individuals having access. Out of 200 unknowns, an average of 92.12% unknowns were rejected and the remaining 7.88% were considered known.
Generative adversarial networks have been widely developed to generate new data, and they have been used for several different applications. Some networks have been developed to classify data at the discriminator level, either by modifying the loss function or by adding a classifier. In this paper, the generative classifier pix2pix, a classifier based on generative adversarial networks, specifically the pix2pix, has been introduced. The classification is done without the need to keep the discriminator or to add additional networks, only the generator is used to classify the data. This classification requires the preparation of a reference dataset. The generative classifier pix2pix was applied to the GC character recognition task using 50000 images for training, it achieved 99.36%. It was also applied to the ORL face recognition task using 360 images for training, and it achieved an average of 97.99%.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.