IntroductionThe spiking neural network is considered one of the best neural networks nowadays with its computational model aims to understand and replicate human capabilities. By replicating a special class of artificial neural networks in which patterns of neurons communicate via sequences of spikes, researchers believe this technique is best for face recognition, facial expression recognition, or emotion detection. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay The work of C. Du, Y. Nan, R.Yan (2017) proved this. Their paper proposes a spiking neural network network architecture for face recognition, this network consists of three parts: feature extraction, coding and classification. For feature extraction they used HMAX model with four layers to extract facial features and then encode all features into suitable spike trains and Tempotron learning rule was used for less calculations. In the experiment they used four databases: Yale, Extend yale B, ORL and FERET. The study by A. Taherkhani (2018) addressed the challenging task of training a population of stimulating neurons in a multilayer network for a precise time, and the delayed learning of the SNN was not carried out in depth. The paper proposes a biologically plausible supervised learning algorithm for learning more precisely timed multiple spikes in a multilayer spike neural network. Train the SNN through the synergy between learning delay and weight. The proposed method shows that it can achieve higher accuracy than single layer neural network. The result shows that a large number of desired peaks can reduce the accuracy of the method. He also said that it is possible to extend the algorithm to multiple levels. However, most layers may reduce the effect of training previous layers on the output. The researcher wants to improve the algorithm in terms of performance and computation. The article by Q. Fu et.al (2017), which improves the performance of the Spiking neural network learning algorithm. It proposes three methods to improve the learning algorithm of Spiking neural network: it includes back propagation of inertia term, adaptive learning and measurement function modification method. In all four methods, including the original algorithm used, the result shows that adaptive learning has the highest accuracy rate of 90%, it also shows that the original algorithm has the lowest accuracy rate compared to Three methods proposed in the article achieved better performance than the original algorithm. Facial expression recognition When we talk about “facial expression” in the field of research, great researchers think of P. Ekman and his books on emotions based on a person's facial expression. In his book “Unmasking The Face” together with WV Friesen, they study facial expression and how to identify emotions based on facial expression. They show photographic photos of each of the six emotions: happiness, sadness, surprise, fear, anger and disgust. The question is: are there universal expressions of emotions? When someone is angry, will we use the same expression regardless of their culture, race or language? Paknikar (2008) defines a person's face as the mirror of our mind. Facial expression and its changes provide us with important information about the person's status, sincere temperament and personality. He also added that nowadays activitiesterrorist attacks are growing all over the world and identifying potential provocateurs is a big problem. That's why body language, facial expression, and tone of speech are the best ways to learn about a person's personality. According to Husak (2017), facial expression is an important factor in observing human behavior, he also introduced the rapid facial movements that appear in stressful situations, typically when a person tries to hide his or her emotion called "microexpressions" . In the study by Kabani S., Khan O., Khan, and Tadvi (2015), they classified facial expressions into 5 different types such as joy, anger, sadness, surprise, and excitement. They also used an emotion model that identifies a song based on any 7 types of emotions; joy-surprise, joy-excitement, joy-anger, i.e. sadness, anger, joy and sadness-anger. Hu (2017) stated that efficiency and accuracy are the two main issues in facial expression recognition. Time, computational and spatial complexity are used to measure efficiency, however in measuring accuracy there is high spatial or computational complexity. They also added that there are few other factors that can affect the accuracy such as pose, low resolution, subjectivity, scale and base frame identification. Another suggestion for emotion detection is that Noroozi et. al (2018) studied are body language that can influence the emotional state of a human being, they include facial expression, body posture, gestures and eye movements in body language, these are an important indicator for detecting of emotions. The group of Yaofu, Yang, and Kuai (2012) used a Spiking Neuron Model for facial expression recognition that uses information represented as spike trains. They also added that the main advantage of this model is its computational cost-effectiveness. They also did an experiment where they showed a graphical representation of six universal expressions; joy, anger, sadness, surprise and excitement plus a neutral expression. Note that the subjects have a similar facial expression but they are all racially different and each of them has a variant of the intensity of the expression. After the experiment they discovered that in all six expressions, the happy and surprised expressions are easier to recognize while the fearful expression is the most difficult. In the research of Wi kiat, Tay (2017), they used an emotion analysis solution through computer vision to automatically recognize facial expression using live video. They also studied anxiety and depression considering that these two are included in emotions. They have their own hypotheses that “anxiety” is a subset of the emotion “Fear”. According to SW Chew (September 2013) and his study on facial expression recognition, he stated that an automatic facial expression recognition system contains three fundamental components; Face detection and tracking, mapping signals to more distinct features, and classifying unique feature patterns. The article by N. Sarode and S. Bhatia (2010) which is “Facial Expression Recognition”, in their research, they study about facial expression as it is the best way to detect emotion. They also used a method which is the local 2D appearance based approach for facial feature extraction, radial symmetry transformation for the basis of the algorithm and also creates a dynamic spatio-temporal representation of the face. Overall, the algorithm achieves 81.0%.robustness. For facial images and databases, the work of JL Kiruba and AD Andrushia (2013) which is "Performance Analysis on Learning Algorithm with Various Facial Expressions on Spiking Neural Network" After using Spiking Neural Network for during their research, they also use and compare two facial image databases, the first is JAFFE database which contains 213 images of 7 facial expressions posed by 10 Japanese women while the other is MPI Database which contains various emotional and conversational expressions. The database contains 55 different facial expressions. Finally the result, JAFFE database has the highest overall recognition rate than MPI database. Research by Y. Liu and Y. Chen (2012) stated that automatic facial expression recognition is an interesting and challenging problem. Deriving features from the raw facial image is the key step for a successful approach. In their system they proposed the combined features which are convolutional neural network and centralized binary pattern and then classified it all using vector Support machine. They also practiced two datasets: extended Cohn-Kanade dataset which achieved 97.6% accuracy and JAFFE database with 88.7% accuracy rate with the help of CNN-CBP. MB Mariappan, M. Suk, and B. Prabhakaran (December 2012) created a multimedia content recommendation system based on users' facial expression. The system called “FaceFetch” which understands the user's current emotional state (Happiness, Anger, Sadness, Disgust, Fear and Surprise) through facial expression recognition and generates or recommends multimedia content to the user such as music, films and other videos that may interest the user from the cloud with near real-time performance. They used the ProASM feature extractor which resulted in better accuracy, faster and more robust. The app receives great response from all the users who have tested the system. The technique proposed and used by T. Matlovic, P. Gaspar, R. Moro, J. Simko and M. Bielikova (October 2016) used facial expression and Electroencephalography for emotion detection. First what they did was analyze existing tools that use facial expression recognition for emotion detection. Secondly, they proposed an emotion detection method using electroencephalography (EEG) that employs existing machine learning approaches. They set up an experiment where they ask participants to watch music videos that evoke emotions. Their emotional epoch consists of measuring the brain activity of participants who achieved 53% accuracy in classifying emotions. He also said that the potential of emotion-based automatic music is far-reaching because it provides a deeper understanding of human emotions. Patel et. Al (2012) described music as the "Language of emotions", they also provide an example where there is an 80 year old man and a 12 year old girl, different generations, different musical tastes but same result of emotions after listening music as if they could both be happy after listening to it but they listen to different genres of music. Their system aimed to meet the needs of music lovers by using facial recognition and saving time browsing and searching from a music player. P. Oliveira (2013) studies the musical system for emotional expression. His goal is to find a computational system to control the emotional content of music, so that it gives a specific emotion. He also added that it must be flexible, scalable and independent of musical style..