More learning with less labeling for face recognition

Name: More learning with less labeling for face recognition
Author: Büyüktaş, Barış, Eroğlu Erdem, Ç., Erdem, Tanju

İsim	More learning with less labeling for face recognition
Yazar	Büyüktaş, Barış, Eroğlu Erdem, Ç., Erdem, Tanju
Basım Tarihi:	2023-05
Basım Yeri	- Elsevier
Konu	Face recognition, Active learning, Self-paced learning, Minimum sparse reconstruction, Deep learning
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1051-2004
Kayıt Numarası	b6c06785-edee-4440-9e14-3a35e25c2fd2
Lokasyon	Computer Science
Tarih	2023-05
Notlar	TÜBİTAK
Örnek Metin	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, ASPL-MSR 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].
DOI	10.1016/j.dsp.2023.103915
Cilt	136

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More learning with less labeling for face recognition

Yazar Büyüktaş, Barış, Eroğlu Erdem, Ç., Erdem, Tanju

Basım Tarihi 2023-05

Basım Yeri - Elsevier

Konu Face recognition, Active learning, Self-paced learning, Minimum sparse reconstruction, Deep learning

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1051-2004

Kayıt Numarası b6c06785-edee-4440-9e14-3a35e25c2fd2

Lokasyon Computer Science

Tarih 2023-05

Notlar TÜBİTAK

Örnek Metin 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, ASPL-MSR 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].

DOI 10.1016/j.dsp.2023.103915

Cilt 136