# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 3 FER Databases  
In order to build FER systems that are able to obtain results which can further be compared with related works, researchers working in the FER field have numerous databases at their disposal. Most databases are built on 2D static images or 2D video sequences; however, there are some databases that contain 3D images. An FER system built on a 2D approach has the limitation of handling different poses poorly, since most 2D databases only contain frontal faces. A 3D approach is potentially capable  
Figure 3: Number of papers found for each year during the paper selection stage. Figure 2: Flowchart of the paper selection for this systematic review. of handling the pose variation problem. Most FER databases are labeled with the six basic emotions (anger, disgust, fear, happiness, sadness, and surprise), plus the neutral expression. Some FER databases are built on controlled environments (generally inside a laboratory with controlled lighting conditions), while others are built on uncontrolled or wild environments. Furthermore, the subjects of some FER databases were asked to pose certain emotions towards a reference, while others tried to stimulate spontaneous and genuine facial expressions. This section introduces the most popular databases used in the reviewed works:  
* **The Extended Cohn-Kanade database (CK+)**[50]: contains 593 image sequences of posed and non-posed emotions. In addition, 123 participants were 18 to 50 years of age, 69% female, 81%, Euro-American, 13% Afro-American, and 6% other groups. The images were digitized into either 640 \(\times\) 490 or 640 \(\times\) 480 resolution and are mostly gray. Each sequence was built on frontal views and 30-degree views, starting with a neutral expression up until the peak emotion (last frame of the sequence). Most sequences are labeled with eight emotions: anger, disgust, contempt, fear, neutral, happiness, sadness, and surprise. * **The Japanese Female Facial Expression database (JAFFE)**[51]: contains 213 images of six basic emotions, plus the neutral expression posed by 10 Japanese female models. Each image has been labeled by 60 Japanese subjects. * **Binghamton University 3D Facial Expression database (BU-3 DFE)**[52]: contains 606 3D facial expression sequences captured from 101 subjects. The texture video has a resolution of about 1040 \(\times\) 1329 pixels per frame. The resulting database consists of 58 female and 43 male subjects, with a large variety of ethnic/racial ancestries. This database was built on the six basic emotions, plus the neutral expression. * **Facial Expression Recognition 2013 database (FER-2013)**[53]: was created using the Google image search API to search for images of faces that match a set of 184 emotion-related keywords like \"blissful\", \"enraged\", etc. These keywords were combined with words related to gender, age or ethnicity, leading to 35,887 grayscale images with a 48 \(\times\) 48 resolution, mapped into the six basic emotions, plus the neutral expression. * **Emotion Recognition in the Wild database (EmotiW)**[54]: contains two sub-databases, Acted Facial Expression in the Wild (AFEW) and the Static Facial Expression in the Wild (SFEW). AFEW contains videos (image sequences including audio) and SFEW contains static images. This database was built on the six basic emotions, plus the neutral expression and the image size is 128 \(\times\) 128. * **MMI database**[55]: contains over 2900 videos and high-resolution still images of 75 subjects. It is fully annotated for the presence of AUs in videos, and partially coded on frame-level, indicating for each frame whether an AU is in either the neutral, onset, apex or offset phase. This database was built on six emotions: anger, disgust, fear, happiness, sadness, and surprise. * **eNTERFACE'05 Audiovisual Emotion database**[56]: contains 42 subjects from 14 different nationalities. Among the 42 subjects, 81% were men and the remaining 19% were women.