- Title
- Improved Automatic Face Segmentation and Recognition for Applications with Limited Training Data
- Creator
- Bradshaw, Karen L
- Creator
- Brown, Dane L
- Subject
- To be catalogued
- Date Issued
- 2017
- Date
- 2017
- Type
- text
- Type
- book
- Identifier
- http://hdl.handle.net/10962/460085
- Identifier
- vital:75891
- Identifier
- ISBN 9783319582740
- Identifier
- https://doi.org/10.1007/978-3-319-58274-0_33
- Description
- This paper introduces varied pose angle, a new approach to improve face identification given large pose angles and limited training data. Face landmarks are extracted and used to normalize and segment the face. Our approach does not require face frontalization and achieves consistent results. Results are compared using frontal and non-frontal training images for Eigen and Fisher classification of various face pose angles. Fisher scales better with more training samples only with a high quality dataset. Our approach achieves promising results for three well-known face datasets.
- Format
- 11 pages
- Format
- Publisher
- Springer
- Language
- English
- Relation
- Brown, D., Bradshaw, K. (2017). Improved Automatic Face Segmentation and Recognition for Applications with Limited Training Data. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation. BDAS 2017. Communications in Computer and Information Science, vol 716. Springer, Cham
- Rights
- Authors
- Rights
- Use of this resource is governed by the terms and conditions of the SpringerLink Terms of Use Statement ( https://link.springer.com/termsandconditions)
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Collections
Brown, Dane A (Dr)
Bradshaw, Karen Lee (Assoc Prof)
RU Department of Computer Science
SDG 09. Industry, Innovation and Infrastructure
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Improved Automatic Face Segmentation and Recognition for Applications with Limited Training Data.pdf | 788 KB | Adobe Acrobat PDF | View Details Download |