Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Large age range is a serious obstacle for automatic face recognition. Although many promising results have been reported, it still remains a challenging problem due to significant intra-class variations caused by the aging process. ,  mainly focus on finding an expressive age-invariant feature such that it is robust to intra-personal variance and discriminative to different subjects. To achieve this goal,  map the original feature to a new space in which the feature is robust to noise and large intra-personal variations caused by aging face images. Then  further encode the mapped feature into an age-invariant representation. After mapping and encoding,  get the robust and discriminative feature for the specific purpose of age-invariant face recognition. To show the effectiveness and generalizability of our method,  conduct experiments on two ll-known public domain databases for age-invariant face recognition: Cross-Age Celebrity Dataset (CACD, the largest publicly available cross-age face dataset) and MORPH dataset. Experiments show that our method achieves state-of-the-art results on these two challenging datasets.
['Xiaonan Hou', 'Shouhong Ding', 'Lizhuang Ma']
I am writing a research paper on a idea Robust feature encoding for age-invariant face recognition
[{"from": "human", "value": "I am writing a research paper on a idea Robust feature encoding for age-invariant 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: Large age range is a serious obstacle for automatic face recognition. Although many promising results have been reported, it still remains a challenging problem due to significant intra-class variations caused by the aging process. ,  mainly focus on finding an expressive age-invariant feature such that it is robust to intra-personal variance and discriminative to different subjects. To achieve this goal,  map the original feature to a new space in which the feature is robust to noise and large intra-personal variations caused by aging face images. Then  further encode the mapped feature into an age-invariant representation. After mapping and encoding,  get the robust and discriminative feature for the specific purpose of age-invariant face recognition. To show the effectiveness and generalizability of our method,  conduct experiments on two ll-known public domain databases for age-invariant face recognition: Cross-Age Celebrity Dataset (CACD, the largest publicly available cross-age face dataset) and MORPH dataset. Experiments show that our method achieves state-of-the-art results on these two challenging datasets."}]