Visible and Infrared Face Identification via Sparse Representation

Abstract

We present a facial recognition technique based on facial sparse representation. A dictionary is learned from data, and patches extracted from a face are decomposed in a sparse manner onto this dictionary. We particularly focus on the design of dictionaries that play a crucial role in the final identification rates. Applied to various databases and modalities, we show that this approach gives interesting performances. We propose also a score fusion framework that allows quantifying the saliency classifiers outputs and merging them according to these saliencies.

System to Safeguard the Identity of Persons in Photographs through Cryptography and Steganography Techniques Using Chaos

“…This algorithm requires two orbits for its proper performance, and these orbits can be generated with any chaotic model desired. In this paper, logistic map equation (1) was implemented to apply confusion to the face pixels, while sine map equation (2) was deployed to apply diffusion to the pixels.…”

“…For example, the OpenFace tool performs this task by implementing neural networks [1]. A facial-characteristics extraction dictionary was also developed to enhance the identification process [2]. In addition, a technique fusing two types of virtual sampling with mirror faces and symmetrical faces, added to the original one, was proposed to correct the issue with insufficient samples in the training period and enhanced the recognition rate [3].…”