Facial Smile Detection Based on Deep Learning Features Authors: Kaihao Zhang, Yongzhen Huang, Hong Wu and Liang Wang Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences

Outline Introduction Method Experiments Conclusions 2/15

Outline Introduction Method Experiments Conclusions 3/15

Introduction Facial smile detection play an important role in understanding human emotions. Applications – Smiling payment – Patient monitoring – Photo selection Extracting powerful facial expression features is a challenging problem 4/15

Introduction Traditional Methods – Hand-crafted features 5/15 Hand-crafted features (LBP, HOG, SIFT) SVM Our Methods – Deep learning features Deep Convolutional Neural Networks Softmax

Introduction Main Contributions: – Propose a new CNN structure to learn facial smile features – Use two loss functions to train our model 6/15

Outline Introduction Method Experiments Conclusions 7/15

Method 8/15 Our framework

Method 9/15 Loss Functions

Outline Introduction Method Experiments Conclusions 10/15

Experiments Dataset – GENKI-4K dataset : ≈4,000 images with a wide range of subjects with different ages and races. Results 11/15

Qualitative results 12/15 Smile detection accuracy of our proposed structure of CNN versus different number of images on the GENKI-4K database. The two loss functions are weighted by a value of k.

Outline Introduction Method Experiments Conclusions 13/15

Conclusions A new structure of CNN is present to learn facial smile features. Utilize two loss functions to train our model. Experimental results show that our method outperforms the state-of-the-art methods 14/15

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