8,221 research outputs found
Smile detection in the wild based on transfer learning
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. 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. Unlike previous works which use
either hand-engineered features or train deep convolutional networks from
scratch, a well-trained deep face recognition model is explored and fine-tuned
for smile detection in the wild. Three different models are built as a result
of fine-tuning the face recognition model with different inputs, including
aligned, unaligned and grayscale images generated from the GENKI-4K dataset.
Experiments show that the proposed approach achieves improved state-of-the-art
performance. Robustness of the model to noise and blur artifacts is also
evaluated in this paper
Circle-based Eye Center Localization (CECL)
We propose an improved eye center localization method based on the Hough
transform, called Circle-based Eye Center Localization (CECL) that is simple,
robust, and achieves accuracy on a par with typically more complex
state-of-the-art methods. The CECL method relies on color and shape cues that
distinguish the iris from other facial structures. The accuracy of the CECL
method is demonstrated through a comparison with 15 state-of-the-art eye center
localization methods against five error thresholds, as reported in the
literature. The CECL method achieved an accuracy of 80.8% to 99.4% and ranked
first for 2 of the 5 thresholds. It is concluded that the CECL method offers an
attractive alternative to existing methods for automatic eye center
localization.Comment: Published and presented at The 14th IAPR International Conference on
Machine Vision Applications, 2015. http://www.mva-org.jp/mva2015
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