Curriculum learning for face recognition

Name: Curriculum learning for face recognition
Author: Büyüktaş, Barış, Erdem, Ç. E., Erdem, Tanju

İsim	Curriculum learning for face recognition
Yazar	Büyüktaş, Barış, Erdem, Ç. E., Erdem, Tanju
Basım Tarihi:	2021
Basım Yeri	- IEEE
Konu	Curriculum learning, Deep learning, Face recognition
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2076-1465
Kayıt Numarası	76b63415-6eeb-4e31-870f-bf2dc602839a
Lokasyon	Computer Science
Tarih	2021
Örnek Metin	We present a novel curriculum learning (CL) algorithm for face recognition using convolutional neural networks. Curriculum learning is inspired by the fact that humans learn better, when the presented information is organized in a way that covers the easy concepts first, followed by more complex ones. It has been shown in the literature that that CL is also beneficial for machine learning tasks by enabling convergence to a better local minimum. In the proposed CL algorithm for face recognition, we divide the training set of face images into subsets of increasing difficulty based on the head pose angle obtained from the absolute sum of yaw, pitch and roll angles. These subsets are introduced to the deep CNN in order of increasing difficulty. Experimental results on the large-scale CASIA-WebFace-Sub dataset show that the increase in face recognition accuracy is statistically significant when CL is used, as compared to organizing the training data in random batches.
DOI	10.23919/Eusipco47968.2020.9287639

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Curriculum learning for face recognition

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

Basım Tarihi 2021

Basım Yeri - IEEE

Konu Curriculum learning, Deep learning, Face recognition

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2076-1465

Kayıt Numarası 76b63415-6eeb-4e31-870f-bf2dc602839a

Lokasyon Computer Science

Tarih 2021

Örnek Metin We present a novel curriculum learning (CL) algorithm for face recognition using convolutional neural networks. Curriculum learning is inspired by the fact that humans learn better, when the presented information is organized in a way that covers the easy concepts first, followed by more complex ones. It has been shown in the literature that that CL is also beneficial for machine learning tasks by enabling convergence to a better local minimum. In the proposed CL algorithm for face recognition, we divide the training set of face images into subsets of increasing difficulty based on the head pose angle obtained from the absolute sum of yaw, pitch and roll angles. These subsets are introduced to the deep CNN in order of increasing difficulty. Experimental results on the large-scale CASIA-WebFace-Sub dataset show that the increase in face recognition accuracy is statistically significant when CL is used, as compared to organizing the training data in random batches.

DOI 10.23919/Eusipco47968.2020.9287639