A novel AI therapy for depression counseling using face emotion techniques

Nixon, Daniel W.; Mallappa, Viswanatha Vanjre; Petli, Vishwanath; Hosgurmath, Sangamesh; K, Shashi Kiran

doi:10.1016/j.gltp.2022.03.008

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…We encounter depression or stress in educational activities, competitive/challenging tasks, work pressure, family consequences, various types of coping with human relationships, health disorders, old age, etc. [1]. Through human facial expressions and emotional recognition, it takes on great importance at this time, because it can capture people's behaviors, feelings, and intentions [2].…”

Section: Introductionmentioning

confidence: 99%

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Face Emotion Recognition System for Depression Detection Using AI Techniques

Singh,

Srivastava

2024

IJRASET

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The human face is an essential aspect of an individual's body. It plays a crucial function in detecting and identifying emotions since the face is where a person exhibits all of their fundamental emotions. Through human emotions, we solve different types of problems. Like healthcare, security, business, and education. The purpose of this paper is to present the detection of depression in the mental health sector. Depression or stress is faced by most of the population all over the world for many reasons and at different stages of life. As the current human life is a busy life cycle, a person gets depressed or stressed in their daily life. Depression may be found in educational activities, competitive or challenging tasks, employment pressure, family consequences, different sorts of human connection management, health issues, old age, and other situations. Artificial intelligence and deep learning approaches are suggested in this study to assess depression. This research is useful for analyzing the mental health of every employer and psychologist when counselling their patients. Here, we propose a deep convolutional neural network (DCNN) model. This model can classify two types of human facial emotions. Which is based on positive and negative emotions. This model is trained and tested using the FER-2013 dataset. The data set used for experimentation is a FER (Facial Expression Recognition) dataset available in the KAGGLE repository. The implementation environment includes Keras, TensorFlow, and OpenCV Python packages. The result includes the emotion detection accuracy between the training and test phases. The average accuracy achieved was 77%.

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Section: Introductionmentioning

confidence: 99%

“…After that, it contrasts movement data with learned emotions [3]. To address complicated issues, the Neural Network (NN), Deep Neural Network (DNN), and Convolutional Neural Network (CNN) use new and enhanced techniques [1]. In the developing world, face identification using image processing has several applications.…”

Section: Introductionmentioning

confidence: 99%

Face Emotion Recognition System for Depression Detection Using AI Techniques

Singh,

Srivastava

2024

IJRASET

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Early detection of depression through facial expression recognition and electroencephalogram-based artificial intelligence-assisted graphical user interface

Kumar,

Das,

Singh

2024

Neural Comput & Applic

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Multi-class Facial Emotion Expression Identification Using DL-Based Feature Extraction with Classification Models

Anand,

Babu

2024

Int J Comput Intell Syst

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Facial expression detection from images and videos has recently gained attention due to the wide variety of applications it has found in the field of computer vision such as advanced driving assistance systems (ADAS), augmented and virtual reality (AR/VR), video retrieval, and security systems. Facial terms, body language, hand gestures, and eye contact have all been researched as a means of deciphering and understanding human emotions. Automated facial expression recognition (FER) is a significant visual recognition procedure because human emotions are a worldwide signal used in non-verbal communication. The six primary universal manifestations of emotion are characterized as happiness, sadness, anger, contempt, fear, and surprise. While the accuracy of deep learning (DL)-based approaches has improved significantly across many domains, automated FER remains a difficult undertaking, especially when it comes to real-world applications. In this research work, two publicly available datasets such as FER2013 and EMOTIC are considered for validation process. Initially, pre-processing includes histogram equalization, image normalization and face detection using Multi-task Cascaded Convolutional Network (MT-CNN) is used. Then, DL-based EfficinetNetB0 is used to extract the features of pre-processed images for further process. Finally, the Weighted Kernel Extreme Learning Machine (WKELM) is used for classification of emotions, where the kernel parameters are optimized by Red Fox Optimizer (RFO). From the experimental analysis, the proposed model achieved 95.82% of accuracy, 95.81% of F1-score and 95% of recall for the testing data.

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A novel AI therapy for depression counseling using face emotion techniques

Cited by 4 publications

References 11 publications

Face Emotion Recognition System for Depression Detection Using AI Techniques

Face Emotion Recognition System for Depression Detection Using AI Techniques

Early detection of depression through facial expression recognition and electroencephalogram-based artificial intelligence-assisted graphical user interface

Multi-class Facial Emotion Expression Identification Using DL-Based Feature Extraction with Classification Models

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