The article discusses the development and significance of the CMU-Pittsburgh AU-Coded Facial Expression Image Database, which is designed to address the limitations of previous facial expression analysis studies. The authors highlight the need for a comprehensive database that captures various dimensions of facial expression, including the level of description, transitions among expressions, eliciting conditions, reliability and validity of data, individual differences, head orientation, scene complexity, image characteristics, and the relation to non-verbal behavior. The database includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, covering multiple primary FACS action units. It is the most extensive resource available for comparative studies in facial expression analysis. The article also outlines the challenges and future plans for extending the database, such as incorporating emotion analysis, surgical applications, and omniview facial analysis. The authors emphasize the importance of this database in advancing the field of facial expression analysis and its potential for robust method development and testing.The article discusses the development and significance of the CMU-Pittsburgh AU-Coded Facial Expression Image Database, which is designed to address the limitations of previous facial expression analysis studies. The authors highlight the need for a comprehensive database that captures various dimensions of facial expression, including the level of description, transitions among expressions, eliciting conditions, reliability and validity of data, individual differences, head orientation, scene complexity, image characteristics, and the relation to non-verbal behavior. The database includes 2105 digitized image sequences from 182 adult subjects of varying ethnicity, covering multiple primary FACS action units. It is the most extensive resource available for comparative studies in facial expression analysis. The article also outlines the challenges and future plans for extending the database, such as incorporating emotion analysis, surgical applications, and omniview facial analysis. The authors emphasize the importance of this database in advancing the field of facial expression analysis and its potential for robust method development and testing.