Deep Learning in Biometrics
By Angshul Majumdar
"Deep Learning in Biometrics" is published by CRC Press in Mar 12, 2018 - Milton and it has 328 pages.
“Deep Learning in Biometrics” Metadata:
- Title: Deep Learning in Biometrics
- Author: Angshul Majumdar
- Number of Pages: 328
- Publisher: CRC Press
- Publish Date: Mar 12, 2018
- Publish Location: Milton
“Deep Learning in Biometrics” Subjects and Themes:
- Subjects: ➤ Biometry - Machine learning - Biometric identification - Human face recognition (Computer science) - Biometric Identification - Machine Learning - Identification biométrique - Reconnaissance faciale (Informatique) - SCIENCE - Life Sciences - General - TECHNOLOGY & ENGINEERING - Electronics
Edition Specifications:
- Format: hardcover
Edition Identifiers:
- The Open Library ID: OL27842546M - OL20593503W
- Online Computer Library Center (OCLC) ID: 1019715986
- ISBN-13: 9781138578234 - 9781351264983
- ISBN-10: 1138578231
- All ISBNs: 1138578231 - 9781138578234 - 9781351264983
AI-generated Review of “Deep Learning in Biometrics”:
"Deep Learning in Biometrics" Description:
Open Data:
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Editors -- Contributors -- 1: Deep Learning: Fundamentals and Beyond -- 2: Unconstrained Face Identification and Verification Using Deep Convolutional Features -- 3: Deep Siamese Convolutional Neural Networks for Identical Twins and Look-Alike Identification -- 4: Tackling the Optimization and Precision Weakness of Deep Cascaded Regression for Facial Key-Point Localization -- 5: Learning Deep Metrics for Person Reidentification -- 6: Deep Face-Representation Learning for Kinship Verification -- 7: Whatâs Hiding in My Deep Features? -- 8: Stacked Correlation Filters -- 9: Learning Representations for Unconstrained Fingerprint Recognition -- 10: Person Identification Using Handwriting Dynamics and Convolutional Neural Networks -- 11: Counteracting Presentation Attacks in Face, Fingerprint, and Iris Recognition -- 12: Fingervein Presentation Attack Detection Using Transferable Features from Deep Convolution Neural Networks -- Index
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