This is the machine learning project in computer vision. We choose to focus on human sentiment analysis. Please see the notebook for our report and summary. You can find our models in the python files in the repo. There is a in-depth introduction in the project presentation slides.
Bohan Wu, Harry Qi, Kun Liang, Naiqiao Ye
Can machine learn human faces and expressions from images?
Please See 601_675_Final_Project_Submission.ipynb and project_presentation_slides.pdf to see more details about the models and dataset
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Eidinger, E., Enbar, R., & Hassner, T. (2014). Age and gender estimation of unfiltered faces. IEEE Transactions on Information Forensics and Security, 9(12), 2170–2179. https://doi.org/10.1109/tifs.2014.2359646
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Zadra, Jonathan R, and Gerald L Clore. “Emotion and perception: the role of affective information.” Wiley interdisciplinary reviews. Cognitive science vol. 2,6 (2011): 676-685. doi:10.1002/wcs.147
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Peters E, Hess TM, Västfjäll D, Auman C. Adult age differences in dual information processes: Implications for the role of affective and deliberative processes in older adults’ decision making. Perspectives on Psychological Science. 2007;2(1):1–23. doi: 10.1111/j.1745-6916.2007.00025.x.
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Goodfellow, Ian J., et al. “Challenges in Representation Learning: A Report on Three Machine Learning Contests.” Neural Networks, vol. 64, 2015, pp. 59–63., https://doi.org/10.1016/j.neunet.2014.09.005.
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Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).