Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning
作者：
Highlights：
• Equidistant prototypes embedding that maximizes the minimum one-against-the-rest margin between the classes.
• A parameter-free model for the practical usage on one sample problem.
• Effective generic learning based on the training samples collected from a different database.
• An incremental learning algorithm that makes the model efficiently updated.
• Extensive experimental results on the Extended Yale B, CMU PIE, AR, and FERET databases.
摘要
Highlights•Equidistant prototypes embedding that maximizes the minimum one-against-the-rest margin between the classes.•A parameter-free model for the practical usage on one sample problem.•Effective generic learning based on the training samples collected from a different database.•An incremental learning algorithm that makes the model efficiently updated.•Extensive experimental results on the Extended Yale B, CMU PIE, AR, and FERET databases.
论文关键词：Face recognition,One sample problem,Linear regression,Feature extraction,Generic learning
论文评审过程：Received 20 November 2012, Revised 3 April 2014, Accepted 23 June 2014, Available online 2 July 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.06.020
