Smile Detection in the Wild Based on Transfer Learning

Abstract

Smile Detection in the Wild Based on Transfer Learning project report on web mining Smile detection from unconstrained facial images is a specialized and challenging problem. As one of the most informative expressions, smiles convey basic underlying emotions, such as happiness and satisfaction, which lead to multiple applications, e.g., human behavior analysis and interactive controlling.

Smile Detection in the Wild Based on Transfer Learning project report on web mining Compared to the size of databases for face recognition, far less labeled data is available for training smile detection systems. To leverage the large amount of labeled data from face recognition datasets and to alleviate overfitting on smile detection, an efficient transfer learning-based smile detection approach is proposed in this paper.

Conclusion

Smile Detection in the Wild Based on Transfer Learning Project report on web mining In our research we aimed to improve smile detection performance on individual faces by applying transfer learning project on web mining as an additional step in the machine learning process. We started out with training a Support Vector Machine on a large generic set of smile data, called the auxiliary data, and used this to train an Adaptive SVM, together with a subset of data of the target face, called the primary data.

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