# Facial Expression Recognition Based on Discrete Separable Shearlet Transform and Feature Selection
## 4 Results and Discussion
### Experimental Database  
JAFFE dataset: the JAFFE dataset contains 213 images of seven facial expressions (six basic facial expressions + one neutral) posed by 10 Japanese female models. Each image was rated on six emotion adjectives by 60 Japanese subjects. The database was planned and assembled by [PERSON], [PERSON], and [PERSON]. The photos were taken at the psychology department in Kyushu University. The images in the JAFFE dataset are all positive faces, and the original images are adjusted and pruned to make the position of eyes and size of the faces roughly the same. The illumination is all positive light sources but the intensity of illumination is different. Because the expression database is open access and the expression calibration is very standard, it is used for training and testing in most articles on expression recognition nowadays. All 213 images of the JAFFE database were used for six-class expression recognition in this study. For each subject, we randomly chose 14 images for training and used the rest for testing. Each time, data from a single subject were reserved as the validation data for testing the algorithm proposed in this paper, whereas the data for the remaining nine subjects were used as the training data. CK+ dataset: the Cohn-Kanade AU-Coded Facial Expression Database is for research in automatic facial image analysis and synthesis and for perceptual studies. Cohn-Kanade is available in two versions and a third is in preparation, and CK+ dataset is the second version. It includes both posed and non-posed (spontaneous) expressions and additional types of metadata, and it consists of 123 university students aged 18-30 years, of which 65% are female, 15% are African-American, and 3% are Asian or Latino. This dataset is much larger than JAFFE and is available free of charge. The database contains 593 image sequences, of which 327 have emotion labels. This database is a popular database in facial expression recognition. Many articles use this database for training and testing. We selected 320 image sequences from the CK+ dataset. The only selection criterion was that a sequence could be labeled as one of the six basic emotions. The sequences come from 96 subjects, with one to six emotions per subject. Because we studied facial expression recognition based on static images in this paper, for each sequence, the neutral face and three peak expression frames were used for prototypic expression recognition, resulting in 960 images (i.e., 108 anger, 120 disgust, 99 fear, 282 joy, 126 sadness, and 225 surprise). The peak expression frame was chosen because it reflects the best state of expression. Therefore, we can extract the features that best reflect this expression. More precisely, we distributed the images randomly into 10 groups with roughly equal numbers of subjects. Nine groups were used as the training data to train classifiers, and the remaining group was used as the test data. MMI dataset: the MMI facial expression database is an ongoing project that aims to deliver large volumes of visual data of facial expressions to the facial expression analysis community. A major issue hindering new developments in the area of automatic human behavior analysis in general, and affect recognition in particular, is the lack of databases with displays of behavior and affect. To address this problem, the MMI facial expression dataset was conceived in 2002 as a resource for building and evaluating facial expression recognition algorithms. The database addresses a number of key omissions in other databases of facial expressions. In particular, it contains recordings of the full temporal pattern of facial expressions ranging from neutral, through a series of onset, apex, and offset phases, and back again to a neutral face. The database consists of over 290 videos and high-resolution still images of 75 subjects.