Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Semi-supervised face recognition using both labelled and unlabelled data has received considerable interest in recent years. Co-training is one of the most ll-known semi-supervised learning methods, but its application in face recognition almost remains unexplored because its assumption of conditional independence can be rarely satisfied beten two facial features. Hover, even if two facial features are not completely independent, their different characteristics produce a so-called “classification margin” beten two classifiers based on them, and hence there is the possibility of mutual training. ,  report a semi-supervised face recognition algorithm which applies co-training on two classifiers based on Linear Discriminant Analysis (LDA) and Local Binary Patterns (LBP) features respectively. Experimental results show not only that the proposed co-training algorithm significantly improves the recognition accuracy over supervised methods using only labelled training data, but also demonstrates the superiority of co-training over self-training methods which only use one facial feature.
['Xuran Zhao', 'Nicholas W. D. Evans', 'Jean-Luc Dugelay']
I am writing a research paper on a idea A co-training approach to automatic face recognition
[{"from": "human", "value": "I am writing a research paper on a idea A co-training approach to automatic face recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Semi-supervised face recognition using both labelled and unlabelled data has received considerable interest in recent years. Co-training is one of the most ll-known semi-supervised learning methods, but its application in face recognition almost remains unexplored because its assumption of conditional independence can be rarely satisfied beten two facial features. Hover, even if two facial features are not completely independent, their different characteristics produce a so-called \u201cclassification margin\u201d beten two classifiers based on them, and hence there is the possibility of mutual training. ,  report a semi-supervised face recognition algorithm which applies co-training on two classifiers based on Linear Discriminant Analysis (LDA) and Local Binary Patterns (LBP) features respectively. Experimental results show not only that the proposed co-training algorithm significantly improves the recognition accuracy over supervised methods using only labelled training data, but also demonstrates the superiority of co-training over self-training methods which only use one facial feature."}]