High-Precise and Robust Face-Recognition System Based on Optical Parallel Correlator

2005 ◽  
Author(s):  
Kashiko Kodate
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Face Recognition System ◽  
Robust Face Recognition

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

2008 ◽  
pp. 188-211
Author(s):  
Ting Shan ◽  
Abbas Bigdeli ◽  
Brian C. Lovell ◽  
Shaokang Chen
Keyword(s):  
Face Recognition ◽  
Embedded System ◽  
Principal Component ◽  
Recognition System ◽  
Active Appearance Models ◽  
Face Recognition System ◽  
The Face ◽  
The Embedded System ◽  
Computationally Intensive ◽  
Robust Face Recognition

In this chapter, we propose a pose variability compensation technique, which synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so the camera can be used to identify the person holding the phone.

scholarly journals Robust Face Recognition System Based on a Multi-Views Face Database

10.5772/6391 ◽  
2008 ◽  
Author(s):  
Dominique Ginhac ◽  
Fan Yang ◽  
Xiaojuan Liu ◽  
Jianwu Dang ◽  
Michel Paindavoine
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Face Database ◽  
Face Recognition System ◽  
Robust Face Recognition

scholarly journals Thermal Image Generation for Robust Face Recognition

2022 ◽  
Vol 12 (1) ◽  
pp. 497
Author(s):  
Vicente Pavez ◽  
Gabriel Hermosilla ◽  
Francisco Pizarro ◽  
Sebastián Fingerhuth ◽  
Daniel Yunge
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Generator System ◽  
Visible Image ◽  
Thermal Images ◽  
Face Recognition System ◽  
Latent Space ◽  
Visible Images ◽  
Robust Face Recognition ◽  
Thermal Face

This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system.

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