Automatic Detection and Classification of Human Emotion in Real-Time Scenario

Abstract

This work proposes the implementation of the idea of real-time human emotion recognition through digital image processing techniques using CNN. This work presents significant literacy calculations used in facial protestation for exact distinctive verification and acknowledgment that can effectively and capably see sentiments from the vibes of the client. The proposed model gives six probability values based on six different expressions. Large datasets are explored and investigated for training facial emotion recognition model. In support of this work, CNN using Deep learning model, OpenCV, Tensorflow, Keras, Pandas, and Numpy is used for digital computer vision procedures involved, and an lite experiment is conducted for various men and women of different age, race, and colour to descry their feelings and variations for different faces are found. This work is improved in 3 targets as face location, acknowledgment and feeling arrangement. Open CV library, and facial expression images dataset are used in this proposed work. Also python writing computer programs is utilized for computer vision (using webcam) procedures. To demonstrate ongoing adequacy, an investigation is directed for a very long time to distinguish their internal feelings and track down physiological changes for each face. The consequences of the examinations exhibit the idealizations in face investigation framework. At long last, the exhibition of programmed face detection and recognition are measured with very high accuracy and in real-time. This method can be implemented and is widely useful in various domains such as security, schools, colleges and universities, military, airlines, banking etc.

New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review

“…The advantages of choosing a CNN for FER systems include its extremely high level of performance, the elimination of the manual feature extraction requirement since the learning is automatically performed on the training data, and perhaps the most important advantage, which is transfer learning, because CNNs allow subsequent constructions based on initial parts of other pre-trained CNNs [ 34 , 71 , 105 , 106 , 107 , 108 , 109 , 110 ]. Transfer learning can be extremely useful because information learned for one task can be transferred to another task, greatly reducing the processing time by eliminating the need to recollect training data for that given task.…”

Enhancing Facial Detection and Recognition: Leveraging OpenCV and CNNs for Efficient Analysis

“…As cited in [2], Convolutional Neural Networks (CNNs) emerge as one of the algorithms most frequently utilized in the domain of facial recognition.…”

“…Then, the ReLU layer turns negative values to zero. [2] The pooling layer reduces feature map size. Lastly, the fully connected layer is used for classification.…”

“…The capacity to authenticate and recognize individuals using images or video streams has brought about transformative changes in security systems, tailored user experiences, and even marketing methodologies. As referenced in [2], OpenCV, an open-source libraries in computer vision, provides a comprehensive toolkit of tools and algorithms for the creation of advanced facial detection and recognition systems. [3] Facial recognition represents an advanced method for ascertaining an individual's distinctive identity by analyzing their facial characteristics.…”

A Novel Approach for Human Face Extraction and Detection using SAE-AFB-RFCN Framework

“…Different facial expressions can be recognized with this model like neutral, angry, fear, happy, sadness, surprise. The CNN model can be developed using OpenCV, Tensorflow, Keras, Pandas, and Numpy using Python library [3]. More complex ML algorithms have replaced more traditional computer vision techniques in recent years for the purpose of face detection occlusion, illuminations, and a complicated background provide the most difficult problems for face recognition systems to overcome.…”