Simultaneous feature and dictionary learning for image set based face recognition
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This document describes a method called jointly learning the discriminative dictionary and projection (JLDDP) that can simultaneously learn a discriminative dictionary and projection for image-based and video-based face recognition. JLDDP aims to learn a dictionary where each atom corresponds to a class label. It also learns a projection matrix to extract discriminative information from original samples. The method uses Fisher discrimination criteria during learning and classifies samples based on representation error and coding coefficients. Experiments on seven face databases demonstrate the effectiveness of JLDDP.
Simultaneous feature and dictionary learning for image set based face recognition
- 1. 2020 2021 #13/ 19, 1st Floor, Municipal Colony, Kangayanellore Road, Gandhi Nagar, Vellore 6. Off: 0416-2247353 Mo: +91 9500218218 / +91 8220150373 Website: www.shakastech.com, Email - id: shakastech@gmail.com, info@shakastech.com Simultaneous Feature and Dictionary Learning for Image Set Based Face Recognition Abstract Recently, dictionary learning has become an active topic. However, the majority of dictionary learning methods directly employs original or predefined handcrafted features to describe the data, which ignores the intrinsic relationship between the dictionary and features. In this study, we present a method called jointly learning the discriminative dictionary and projection (JLDDP) that can simultaneously learn the discriminative dictionary and projection for both image-based and video-based face recognition. The dictionary can realize a tight correspondence between atoms and class labels. Simultaneously, the projection matrix can extract discriminative information from the original samples. Through adopting the Fisher discrimination criterion, the proposed framework enables a better fit between the learned dictionary and projection. With the representation error and coding coefficients, the classification scheme further improves the discriminative ability of our method. An iterative optimization algorithm is proposed, and the convergence is proved mathematically. Extensive experimental results on seven image-based and video-based face databases demonstrate the validity of JLDDP