# A Comparative Study of Local Descriptors and Classifiers for Facial Expression Recognition
###### Abstract  
Facial Expression Recognition (FER) is a growing area of research due to its numerous applications in market research, video gaming, healthcare, security, e-learning, and robotics. One of the most common frameworks for recognizing facial expressions is by extracting facial features from an image and classifying them as one of several prototypic expressions. Despite the recent advances, it is still a challenging task to develop robust facial expression descriptors. This study aimed to analyze the performances of various local descriptors and classifiers in the FER problem. Several experiments were conducted under different settings, such as varied extraction parameters, different numbers of expressions, and two datasets, to discover the best combinations of local descriptors and classifiers. Of all the considered descriptors, HOG (Histogram of Oriented Gradients) and ALDP (Angled Local Directional Patterns) were some of the most promising, while SVM (Support Vector Machines) and MLP (Multi-Layer Perceptron) were the best among the considered classifiers. The results obtained signify that conventional FER approaches are still comparable to state-of-the-art methods based on deep learning. facial expression recognition; feature extraction; local descriptors; classification +
Footnote †: journal: Medical  
identify two main trends in the development of FER systems. On the one hand, there are FER systems based on the extraction and classification of handcrafted features, and on the other, we have approaches based on deep learning. Handcrafted techniques rely on domain-specific knowledge about the human face, such as facial muscle deformations and the movement of facial components, such as raising the eyes and the opening/closing of the mouth during emotion elicitation. Within the handcrafted techniques, the geometrical features were developed to describe the shape and location of facial components [8]. Some work was also undertaken to reconstruct Action Units and interpret their combinations as facial expressions [9]. Geometric-based FER systems were difficult to develop because they required the accurate labeling of Action Units [6] and landmarks, which is time-consuming and tedious. During this time, appearance-based FER systems were being developed, giving rise to local descriptors for facial expression representation. Local descriptors were used to extract texture information such as edges, corners, and spots that make up facial expressions and achieved the same or better results while being less tedious to develop than the geometric features. Recently, deep neural networks have been studied for facial expression recognition [10; 11]. Unlike traditional techniques, deep learning includes little knowledge about facial structure and appearance. Deep learning systems automatically detect and extract facial features based on a series of layers, such as convolution, pooling, and fully-connected layers. This field is attracting more and more attention due to the improved recognition capacity offered by deep neural networks for static and sequence-based recognition. Mainly, CNN (Convolution Neural Networks) is the basic model for extracting more discriminative spatial features from face images, while RNN (Recurrent Neural Networks) and LSTM (Long Short-Term Memory) networks are suitable for characterizing the spatio-temporal features of sequences [12]. In the literature, there have been a few attempts to compare the different methods based on local descriptors and traditional classifiers for facial expression recognition. In 2018, [PERSON] and [PERSON] [13] studied 27 local descriptors under different conditions, such as varying image resolutions and the number of sub-regions. Two classifiers were used to recognize a different number of expressions on four facial expression databases. Their results were comparable to the state-of-the-art deep learning approaches on well-known datasets. [PERSON] et al. [14] studied the independent performance of 46 LBP variants for facial expression recognition.