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Deep Learning Face Representation by...

Sun, Yi.

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Deep Learning Face Representation by Joint Identification-Verification.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning Face Representation by Joint Identification-Verification./
Author:	Sun, Yi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2015,
Description:	124 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
Contained By:	Dissertation Abstracts International78-05B(E).
Subject:	Artificial intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10297334
ISBN:	9781369410396

Deep Learning Face Representation by Joint Identification-Verification.
Sun, Yi.

Deep Learning Face Representation by Joint Identification-Verification. - Ann Arbor : ProQuest Dissertations & Theses, 2015 - 124 p.

Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2015.

This thesis studies how to make full use of the superb learning capacity of deep neural networks to learn effective feature representations for face recognition. In particular, we propose three effective supervisory signals for deep feature learning, i.e., verification, identification, and joint identification-verification supervisory signals. Verification is the binary classification task of classifying pairs of training face images as being the same person or not. It greatly reduces the intra-personal variations in the face representation. Identification classifies each training face image into one of N different identities. By classifying a large number of identities (e.g., N ≈ 10,000) simultaneously, the last hidden layer of deep neural networks would extract features with rich identity-related (inter-personal) variations. Joint identification-verification combines the first two supervisory signals. Features learned by joint identification-verification supervisory signals exhibit both rich interpersonal variations and small intra-personal variations. With these proposed deep feature learning techniques, we greatly advances the face recognition performance on the extensively evaluated LFW benchmarks. Finally, we empirically investigate three properties of the learned deep feature representations critical for the high performance: sparsity, selectiveness, and robustness.

ISBN: 9781369410396Subjects--Topical Terms:

516317
Artificial intelligence.

Deep Learning Face Representation by Joint Identification-Verification.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2015
300 $a 124 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-05(E), Section: B.
500 $a Adviser: Jianwei Huang.
502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2015.
520 $a This thesis studies how to make full use of the superb learning capacity of deep neural networks to learn effective feature representations for face recognition. In particular, we propose three effective supervisory signals for deep feature learning, i.e., verification, identification, and joint identification-verification supervisory signals. Verification is the binary classification task of classifying pairs of training face images as being the same person or not. It greatly reduces the intra-personal variations in the face representation. Identification classifies each training face image into one of N different identities. By classifying a large number of identities (e.g., N ≈ 10,000) simultaneously, the last hidden layer of deep neural networks would extract features with rich identity-related (inter-personal) variations. Joint identification-verification combines the first two supervisory signals. Features learned by joint identification-verification supervisory signals exhibit both rich interpersonal variations and small intra-personal variations. With these proposed deep feature learning techniques, we greatly advances the face recognition performance on the extensively evaluated LFW benchmarks. Finally, we empirically investigate three properties of the learned deep feature representations critical for the high performance: sparsity, selectiveness, and robustness.
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