## 3 Results
### Unimodal Data Approaches
#### 3.2.1 Unimodal Physical Approaches  
Unimodal facial emotion recognition has been extensively studied in recent years. In this study, the aim is to understand the new trends in terms of databases, models, and applications. Table 4 lists the titles of articles with a specific unimodal focus on facial emotion recognition (from the studies included in this systematic review) that describe the databases they used for their study. This list reveals which databases are most frequently utilized. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Article Title** & **Databases Used** & **Ref.** \\ \hline AffectNet: A Database for Facial Expression, Valence, and Arousal & AffectNet & [35] \\ Computing in the Wild. & Video-Based Depression Level Analysis by Encoding Deep & AVEC2013, AVEC2014 & [36] \\ Spatiotemporal Features. & & \\ Exploiting Multi-CNN Features in CNN-RNN Based Dimensional Emotion & Aff-Wild, Aff-Wild2, OMG & [37] \\ Recognition on the OMG in-the-Wild Dataset. & CK+, JAFFE, MMI, Oulu-CASIA, AffectNet, & [38] \\ A Deeper Look at Facial Expression Dataset Bias. & FER2013, RAF-DB 2.0, SFEW 2.0 & [39] \\ Automatic Recognition of Facial Displays of Unfelt Emotions. & CK+, OULU-CASIA, BP4D & [39] \\ Spatio-Temporal Encoder-Decoder Fully Convolutional Network for & OMG, RECOLA, SEWA & [6] \\ Video-Based Dimensional Emotion Recognition. & CK+, FER2013, JAFFE, KDEF & [40] \\ Efficient Net-XGBoost: An Implementation for Facial Emotion Recognition Using Transfer Learning. & CK+, FER2013, JAFFE, KDEF & [41] \\ Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People. & AffectNet & [42] \\ Facial Feature Extraction Using a Symmetric Inline Matrix-LBP Variant for Emotion Recognition. & JAFFE & [43] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Articles focused on unimodal facial emotion recognition and the databases they used. Figure 4: Quantity of articles from unimodal categories. Some of the databases included in the articles listed in Table 4 are considered benchmarks in the field facial emotion recognition. Among the most frequently repeated databases in the studies included in this review, the following can be identified: JAFFE (1997), CK+ [49] Oulu-CASIA [50] FER2013 (2013), OMG-One-Minute Gradual-Emotion Dataset, [51], and AffectNet [35]. Below is a brief description of each of these, highlighting their advantages and limitations. JAFFE (Japanese Female Facial Expression 1997): JAFFE is a database that focuses on facial expressions of Japanese women. It contains images of seven basic emotions, including happiness, sadness, anger, surprise, fear, disgust, and neutrality. The images were captured in a controlled environment where subjects were provided with visual stimuli or specific instructions to express basic emotions. This database is useful for studying cultural differences in emotional expression. JAFFE was created at the University of Kyoto in Japan in the 1990s and was published in 1997. CK+ (Cohn-Kanade Database) [49]: CK+ is a database of facial expression images featuring actors representing basic emotions such as happiness, sadness, anger, surprise, fear, contempt, and neutrality. The images were captured in controlled conditions and are accurately labeled. CK+ is renowned for its quality and precision in emotional labeling. Emotions were not artificially induced in a controlled environment; instead, they were captured as actors genuinely expressed them. It was developed in the University of Pittsburgh's laboratory and released in multiple phases over time, with the complete version made public in 2010. Oulu-CASIA [50]: Developed by the University of Oulu in Finland in 2011. It focuses on the authenticity of facial expressions and contains images of Finnish subjects recorded under controlled conditions while expressing both genuine and fake facial emotions. It is valuable for research involving the detection of genuine and fake facial expressions, which is relevant in lie detection applications.