More learning with less labeling for face recognition - EROĞLU ERDEM, ÇİĞDEM (My University, 2023)
In this paper, we propose an improved face recognition framework where the training is started with a small set of human annotated face images and then new images are incorporated into the training set with minimum human annotation effort. In order to minimize the human annotation effort for new images, the proposed framework combines three different strategies, namely self-paced learning (SPL), active learning (AL), and minimum sparse reconstruction (MSR). As in the recently proposed ASPL framework [1], SPL is used for automatic annotation of easy images, for which the classifiers are highly confident and AL is used to request the help of an expert for annotating difficult or low-confidence images. In this work, we propose to use MSR to subsample the low-confidence images based on diversity using minimum sparse reconstruction in order to further reduce the number of images that require human annotation. Thus, the proposed framework provides an improvement over the recently proposed ASPL framework [1] by employing MSR for eliminating “similar” images from the set selected by AL for human annotation. Experimental results on two large-scale datasets, namely CASIA-WebFace-Sub and CACD show that the proposed method called ASPL-MSR can achieve similar face recognition performance by using significantly less expert-annotated data as compared to the state-of-the-art. In particular, ASPLMSR requires manual annotation of only 36.10% and 54.10% of the data in CACD and CASIA-WebFace-Sub datasets, respectively, to achieve the same face recognition performance as the case when the whole training data is used with ground truth labels. The experimental results indicate that the number of manually annotated samples have been reduced by nearly 4% and 2% on the two datasets as compared to ASPL [1].
Keywords: Bilgisayar Bilimleri; Bilgisayarla Görme; Yapay Zeka, Bilgisayarda Öğrenme ve Örüntü Tanıma; Örüntü Tanıma ve Görüntü İşleme; Sinirsel Ağlar; Mühendislik ve Teknoloji; Computer Sciences; Computer Vision; Artificial Intelligence, Computer Learning and Pattern Recognition; Pattern Recognition and Image Processing; Neural Networks; Engineering and Technology; Mühendislik, Bilişim ve Teknoloji (ENG); Bilgisayar Bilimi; BİLGİSAYAR BİLİMİ, YAPAY ZEKA; BİLGİSAYAR BİLİMİ, İNTERDİSİPLİNER UYGULAMALAR; Engineering, Computing & Technology (ENG); COMPUTER SCIENCE; COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS; Yer Bilimlerinde Bilgisayarlar; Bilgisayarla Görme ve Örüntü Tanıma; Bilgisayar Bilimi Uygulamaları; Bilgisayar Grafikleri ve Bilgisayar Destekli Tasarım; Yapay Zeka; Bilgisayar Bilimi (çeşitli); Genel Bilgisayar Bilimi; Fizik Bilimleri; Computers in Earth Sciences; Computer Vision and Pattern Recognition; Computer Science Applications; Computer Graphics and Computer-Aided Design; Artificial Intelligence; Computer Science (miscellaneous); General Computer Science; Physical Sciences; Face recognition; Active learning; Self-paced learning; Minimum sparse reconstruction; Deep learning