Smartphone based emotion recognition and classification

Sneha, H R; Rafi, Mohamed; Kumar, M. V. Manoj; Thomas, Likewin; Annappa, B.

doi:10.1109/icecct.2017.8117872

Cited by 11 publications

(17 citation statements)

References 15 publications

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“…They detected state changes of patients suffering from bipolar disorder. Sneha et al [40] analyzed the textual content of the message and user typing behavior to classify future instances. Hossain et al [41] modeled hybrid features based on bandelet transform [42] and local binary patterns [43] for emotion classification.…”

Section: Visual Signal Based Emotion Classificationmentioning

confidence: 99%

Facial Emotion Recognition Using Hybrid Features

Alreshidi

Ullah

2020

Informatics

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Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ recognition. The framework consists of two machine learning algorithms (for detection and classification) that could be trained offline for real-time applications. Initially, we detect faces in the images by exploring the AdaBoost cascade classifiers. We then extract neighborhood difference features (NDF), which represent the features of a face based on localized appearance information. The NDF models different patterns based on the relationships between neighboring regions themselves instead of considering only intensity information. The study is focused on the seven most important facial expressions that are extensively used in day-to-day life. However, due to the modular design of the framework, it can be extended to classify N number of facial expressions. For facial expression classification, we train a random forest classifier with a latent emotional state that takes care of the mis-/false detection. Additionally, the proposed method is independent of gender and facial skin color for emotion recognition. Moreover, due to the intrinsic design of NDF, the proposed method is illumination and orientation invariant. We evaluate our method on different benchmark datasets and compare it with five reference methods. In terms of accuracy, the proposed method gives 13% and 24% better results than the reference methods on the static facial expressions in the wild (SFEW) and real-world affective faces (RAF) datasets, respectively.

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Section: Visual Signal Based Emotion Classificationmentioning

confidence: 99%

Facial Emotion Recognition Using Hybrid Features

Alreshidi

Ullah

2020

Informatics

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“…Grünerbl et al [26] presented a method considering smartphone sensors for the identification of depressive and panic mental states and recognize state variations of people targeted by bipolar disorder disease. Sneha et al [27] introduced the textual content of the message and user typing behaviour to make a model that easily divides the future instances. Hossain et al [28] introduced a method in which Bandlet transform is used on the face areas, and the resultant subband is partitioned into non-overlapping sections.…”

Section: Literature Reviewmentioning

confidence: 99%

Facial Emotion Recognition using Neighborhood Features

Aljaloud¹,

Ullah²,

Alanazi³

2020

IJACSA

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We present a new method for human facial emotions recognition. For this purpose, initially, we detect faces in the images by using the famous cascade classifiers. Subsequently, we then extract a localized regional descriptor (LRD) which represents the features of a face based on regional appearance encoding. The LRD formulates and models various spatial regional patterns based on the relationships between local areas themselves instead of considering only raw and unprocessed intensity features of an image. To classify facial emotions into various classes of facial emotions, we train a multiclass support vector machine (M-SVM) classifier which recognizes these emotions during the testing stage. Our proposed method takes into account robust features and is independent of gender and facial skin color for emotion recognition. Moreover, our method is illumination and orientation invariant. We assessed our method on two benchmark datasets and compared it with four reference methods. Our proposed method outperformed them considering both the datasets.

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“…Viana et al [10] had built a message classifier based on multinomial Naïve Bayes for online social contexts, while Yadav and Gupta [11] had used Naïve Bayes classifier to analyze user tweets for detection and prevention of self-harm tendencies of the Twitter user. Bashir et al [12] had made automatic text summarization based on feature extraction using Naïve Bayes model, Sneha et al [13] had made a smartphone-based emotion recognition and classification using Naïve Bayes classifier, and Wijaya and Santoso [14] tried to improve the accuracy of Naïve Bayes algorithm for hoax classification using particle swarm optimization. Based on visibility study and previous researches, research of Naive Bayes algorithm on LINE bots to filter chat based on the interests of the organization interest is made in this study.…”

Section: Introductionmentioning

confidence: 99%

Line Bot Chat Filtering using Naïve Bayes Algorithm

Elvina*¹,

Rusli²,

Hansun³

2019

IJITEE

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Abstract: Instant messaging has changed and simplified the way people communicate, whether in professional or personal life. produces spam precision and spam recall as 94.2% and 95.6% respectively. Most communication is done through instant messaging, and it is common for people to miss important information. This is due to the huge amount of incoming message notifications, so users tend to accidentally ignore them. This is also experienced by Universitas Multimedia Nusantara (UMN) student committees who communicate via LINE instant messenger. This research showed LINE bot was made by using the Naive Bayes algorithm to classify between important messages and unimportant messages on the committee group. The Naive Bayes algorithm is a classification algorithm based on probability and statistical methods. The Naive Bayes algorithm is chosen because it is widely implemented in spam filtering; the method is simple and has good accuracy. The classification process is done by calculating the probability of chat in each class based on the value of the word likelihood which generated in the training process. This research

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Smartphone based emotion recognition and classification

Cited by 11 publications

References 15 publications

Facial Emotion Recognition Using Hybrid Features

Facial Emotion Recognition Using Hybrid Features

Facial Emotion Recognition using Neighborhood Features

Line Bot Chat Filtering using Naïve Bayes Algorithm

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