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Multi-task Deep Face Recognition

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Original languageEnglish
Title of host publicationBiometric Recognition - 12th Chinese Conference, CCBR 2017, Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319699226
Publication statusPublished - 2017
Publication typeA4 Article in a conference publication
EventChinese Conference on Biometric Recognition -
Duration: 1 Jan 2000 → …

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceChinese Conference on Biometric Recognition
Period1/01/00 → …


In recent years, deep learning has become one of the most representative and effective techniques in face recognition. Due to the high expense of labelling data, it is costly to collect a large-scale face dataset with accurate label information. For the tasks without sufficient data, deep models cannot be well trained. Generally, parameters of deep models are usually initialized with a pre-trained model, and then fine-tuned on a small dataset of specific task. However, by straightforward fine-tuning, the final model usually does not generalize well. In this paper, we propose a multi-task deep learning (MTDL) method for face recognition. The superiority of the proposed multi-task method is demonstrated by experiments on LFW and CCFD.


  • Convolution neural network, Deep learning, Face recognition, Multi-task

Publication forum classification

Field of science, Statistics Finland