Automatic Facial Analysis is one of the most important field of computer vision due to its significant impacts to the world we currently live in. Among many applications of Automatic Facial Analysis, Facial Alignment and Facial-Based Emotion Recognition are two most prominent tasks considering their roles in this field. That is, the former serves as intermediary steps enabling many higher facial analysis tasks, and the latter provides direct, real-world high level facial-based analysis and applications to the society. Together, they have significant impacts ranging from biometric recognition, facial recognition, health, and many others. These facial analysis tasks are currently even more relevant given the emergence of big-data, that enables rapid development of machine learning based models advancing their current state of the arts accuracy. In regard to this, the uses of video-based data as the part of the development of current datasets have been more frequent. These sequence based data have been explicitly exploited in the other relevant machine learning fields through the use of inherent temporal information, that in contrast, it has not been the case for both of Facial Alignment and Facial-Based Emotion Recognition tasks. Furthermore, the in-the-wild characteristics of the data that exist on the current datasets present additional challenge for developing an accurate system to these tasks. In this talk, I will give some overview of the benefits from incorporating both temporal information and the in-the-wild data characteristics that are largely overlooked on both Facial Alignment and Facial-Based Emotion Recognition.
Duration: 30 min
Speaker: Decky Aspandi Latif