我们的科技
在人机交互领域超过10年的研发和商业合作经验。我们的技术能对情绪进行完美的辨别，具有很高的准确性和提取隐藏情绪的能力。
一个人的情绪状态是在一个人或群体的意图表现的反映。通过对门诊病人和非病人的情绪进行索引和研究，我们能检测出情绪的普遍性，以及对应共存的内在意图。
我们的情感分析可在主流的平台上运行，适用于任何带有摄像头的设备或可穿戴光学设备。计算成本低，在边缘设备上需要不到1GHz的处理量，对带宽要求低。强大的SDK与API库，可通过第三方不断开发各种应用程序。
对多模态语音、生理信号和手势进行进一步的情感计算研究，以体现个人或群体的情感状态，并进行精细化分类。
应用领域和案例
Opsis联合创始人（S. Winkler教授）关于面部表情分析技术的出版物
S. Winkler, L. Zhang, S. Peng.
PersEmoN: A deep network for joint analysis of apparent personality, emotion and their relationship.
IEEE Transactions on Affective Computing, to appear.
S. Winkler, V. Vonikakis, D. Neo.
MorphSet: Augmenting categorical emotion datasets with dimensional affect labels using face morphing.
eprint arXiv:2103.02854, March 2021.
S. Winkler V. Vonikakis.
Identity-invariant facial landmark frontalization for facial expression analysis.
Proc. IEEE International Conference on Image Processing (ICIP), Abu Dhabi, UAE, Oct. 25-28, 2020.
S. Winkler S. Peng, L. Zhang, Y. Ban, M. Fang.
A deep network for arousal-valence emotion prediction with acoustic-visual cues.
One Minute Gradual (OMG) Emotion Behavior Challenge (1st/2nd place), eprint arXiv:1805.00638, May 2018.
S. Winkler V. Vonikakis, Y. Yazıcı, V. D. Nguyen.
Group happiness assessment using geometric features and dataset balancing.
Proc. 18th ACM International Conference on Multimodal Interaction (ICMI), Emotion Recognition in the Wild Challenge (2nd place), Tokyo, Japan, Nov. 12-16, 2016.
S. Winkler, H.-W. Ng, V. D. Nguyen, V. Vonikakis.
Deep learning for emotion recognition on small datasets using transfer learning.
Proc. 17th ACM International Conference on Multimodal Interaction (ICMI), Emotion Recognition in the Wild Challenge (3rd place), Seattle, WA, Nov. 9-13, 2015.
