## 1 Introduction  
In recent days, due to the rapid growth of technology, human-computer interaction has begun to gain courtesy in the research domain. Expression of emotions through facial expressions is an important aspect of human communication that serves social interaction. In the work described by [PERSON] et al. in [1], researchers have validated seven universal feature expressions, namely disgust, anger, happiness, sadness, neutral, fear and surprise. According to facial action coding system [2],various positions of the muscles on the face are responsible for the facial expression as explained by [PERSON] and [PERSON] [3]. The total number of action units in facial action coding system is found to be 46. These actions are associated with defined set of muscle movement [4]. The main use of Facial Emotion Recognition (FER) [5] lies in determining the emotional state of a person which further helps in depicting the mental state identification and mental disorders. Besides, FER also contributes to video indexing and data driven animation [6]. FER system is capable of making human-robot communication much more significant and effective. There are various challenges involved in FER. Predominantly, the camera angle is one of the key issues in data collection part. Wrong camera angle can hamper the recognition accuracy as it can distress the small muscle movements. Besides, there are many types of noises that also affect the minute muscle movements in eyes, nose or other parts of the face. For a robust FER system, the primary need is to design a competent-feature vector. As the features are extracted from facial images showing different human emotions, in many cases, irrelevant or redundant features are generated. This ultimately stretches the dimension of feature set and brings down the overall accuracy in making predictions. Feature selection (FS)--an initiative to optimize feature dimensions undertaken by various researchers--aims in extricating redundant/irrelevant features that do not make any significant contribution in the overall prediction process [7,8]. FS is a useful way for substantial reduction of the size of original-feature vectors used to predict target facial emotions expressed by humans. This not only reduces computation time required to make the prediction, but also improves final accuracy by removing redundant and irrelevant features. Redundant and noisy features lead to misclassification and higher memory requirements, which, in turn, significantly increase model building time [9]. FS requires choosing a best-feature subset from \(2^{N}\) feature subset combinations, where \(N\) is the size of the feature subset [10]. Some of the techniques of FS may prove to be very costly. As a result, in many cases, random search and heuristic search techniques are employed. In [11], an algorithm for unsupervised FS using feature similarity measure, called maximum information compression index was elucidated. Another FS technique was proposed in [12] which uses sequential search methods characterized by dynamically changing the number of features, i.e., \"floating\" methods. The works described in [13, 14, 15, 16] are some other established applications of FS in solving problems like handwriting recognition, handwritten script classification [17], handwritten numeral classification, etc. Another instance of FS, called histogram-based fuzzy ensemble technique was applied to UCI datasets for evaluation in [18]. FS algorithms are segregated into three categories [19]. The filter method applies a statistical or probabilistic approach to assign a scoring to each feature and these features are selected to be either kept or removed from the original-feature set. The wrapper method is often used in conjunction with a machine learning algorithm (which works as a classifier), where the learning algorithm plays the part of the feature validation process and selects an optimal feature subset which enhances the classification accuracy. The hybrid method tends to perform computationally better than wrapper method.