Li Y, Wang S, Zhao Y, Ji Q (2013) simultaneous facial feature tracking and facial expression recognition. Communications in computer and information science, vol 517.

In: Iliadis L, Jayne C (eds) Engineering applications of neural networks. Perikos I, Ziakopoulos E, Hatzilygeroudis I (2015) Recognize emotions from facial expressions using a SVM and neural network schema. Lecture notes in computer science, vol 8868. In: Pecchia L, Chen LL, Nugent C, Bravo J (eds) Ambient assisted living and daily activities. Lozano-Monasor E, López MT, Fernández-Caballero A, Vigo-Bustos F (2014) Facial expression recognition from webcam based on active shape models and support vector machines. Kuo P-H, Li T-HS, Ho Y-F, Lin C-J (2015) Development of an automatic emotional music accompaniment system by fuzzy logic and adaptive partition evolutionary genetic algorithm. Jain DK, Shamsolmoali P, Sehdev P (2019) Extended deep neural network for facial emotion recognition. Jing H, Lun X, Dan L, Zhijie H, Zhiliang W (2015) Cognitive emotion model for eldercare robot in smart home. Communications in computer and information science, vol 841. In: Rameshan R, Arora C, Dutta Roy S (eds) Computer vision, pattern recognition, image processing, and graphics. Sarkar R, Dutta S, Roy A, Saha SK (2018) Emotion based categorization of music using low level features and agglomerative clustering. Olga S, Liu Y, Nguyen MK (2012) Real-time EEG-based emotion recognition for music therapy. In: Hu W (ed) Advances in electric and electronics. In: Consulting psychologists, Palo Alto, CA, USAĭeng JJ, Leung CHC (2012) Music emotion retrieval based on acoustic features. KeywordsĮkman P, Friesen W (1978) Facial action coding system: a technique for the measurement of facial movement. The approach is evaluated using performance measures and noticed that the proposed approach gave better results. Here, convolution neural network is used as deep learning framework to analyze the mood transformation before and after music therapy and further classifies for positive/negative emotion. Initially, deep learning framework is trained with using FER-2013 dataset for emotion recognition and classification, and later, it considers the live stream videos for experimentation. İt is noticed that the music therapy acts as a psychotherapy to recover the person from negative emotions. The approach considers the negative emotions alone and applies music therapy for mood transformation from negative to positive polarity. In this paper, the emotions are recognized and classified based on their polarities into positive and negative emotions using deep learning framework. The people under the influence of negative emotions may take longer time for recovery. In general, a person’s mood varies based on the emotion, which is an internal state of mind and that depends on external events. The emotions such as happy, neutral, and surprise are considered as positive emotions, and disgust, anger, sad, and fear are considered as negative emotions. Further, these emotions are classified as positive and negative emotions. Consulting Psychologists, Palo Alto, CA, USA, 1978) proposed six basic emotions such as fear, disgust, anger, sad, happy, and surprise. Ekman (Ekman P, Friesen W in Facial action coding system: a technique for the measurement of facial movement. The facial components such as eye–eyebrows, mouth, nose, forehead, cheeks play important role in expressing these emotions. Human interact with the outer world and express their emotions using facial expressions.

Mood deep