RAF-ML (Real-world Affective Faces Multi Label)

Real-world Affective Faces Multi Label (RAF-ML) is a multi-label facial expression dataset with around 5K great-diverse facial images downloaded from the Internet with blended emotions and variability in subjects' identity, head poses, lighting conditions and occlusions. During annotation, 315 well-trained annotators are employed to ensure each image can be annotated enough independent times. And images with multi-peak label distribution are selected out to constitute the RAF-ML.

RAF-ML provides 4908 number of real-world images with blended emotions, 6-dimensional expression distribution vector for each image, 5 accurate landmark locations and 37 automatic landmark locations, and baseline classifier outputs for multi-label emotion recognition.

Source: Real-world Affective Faces Multi Label