Real Time Emotion Recognition and Gender Classification

Gogate, Uttara; Parate, Alap; Sah, Shubham; Narayanan, Sagar

doi:10.1109/icsidempc49020.2020.9299633

Cited by 9 publications

(2 citation statements)

References 2 publications

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“…They found that using a CNN model with a deeper network (more layers) produces the best results. The authors of [12] aimed to classify a person's gender and emotions in real time or by using the person's image on a smartphone or a hard copy of the picture. Gender detection was achieved by obtaining the softmax value to identify gender using real-time CNNs.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Gender Classification System in the Era of Pandemic Chaos with Veiled Faces

et al. 2022

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In the world of chaos, the pandemic has driven individuals around the globe to wear face masks for preventing the virus’s transmission, however, this has made it difficult to determine the gender of the person wearing a mask. Gender information is part of soft biometrics, which provides extra information about a person’s identification, thus, identifying a gender based on a veiled face is among the urgent challenges that must be advocated for in the next decade. Therefore, this study exploited various pre-trained deep learning networks (DenseNet121, DenseNet169, ResNet50, ResNet101, Xception, InceptionV3, MobileNetV2, EfficientNetB0, and VGG16) to analyze the effect of the mask while identifying the gender using facial images of human beings. The study comprises two strategies. First, the experimental part involves the training of models using facial images with and without masks, while the second strategy considers images with masks only, to train the pre-trained models. Experimental results reveal that DenseNet121 and Xception networks performed well for both strategies. Besides this, the Inception network outperformed all others by attaining 98.75% accuracy for the first strategy, whereas EfficientNetB0 performed well for the second strategy by securing 97.27%. Moreover, results suggest that facemasks evidently impact the performance of state-of-the-art pre-trained networks for gender classification.

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Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Gender Classification System in the Era of Pandemic Chaos with Veiled Faces

et al. 2022

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“…CNN model is implemented with accuracy reaching 96% for classifying gender in IMDB Dataset and 66% in classifying emotion in FER-2013 dataset. U.Gogate [4] proposed a CNN architecture that helps to classify Emotion of Facial expression with an accuracy of 67% and also classify the gender of person with accuracy of 95% in IMDB.Akash Saravanan et.al [5] uses face images of FER2013 dataset to classify the emotions on a person's face into one of the seven categories.In this paper ,a real time emotion detection system is built for emotion classification with an accuracy reached to 60.58%.…”

Section: Related Workmentioning

confidence: 99%

Real-time Emotion and Gender Classification using Ensemble CNN

Abhinav¹,

Singh²,

Tiwary³

2021

Preprint

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Analyzing expressions on the person's face plays a very vital role in identifying emotions and behavior of a person. Recognizing these Expressions automatically results in a crucial component of natural human-machine interfaces.Therefore research in this field has a wide range of applications in biometric authentication, surveillance systems,emotions to emoticons in various social media platforms.Another application includes conducting customer satisfaction surveys. As we know that the large corporations made huge investments to get feedback and do surveys but fail to get equitable responses . Emotion & Gender recognition through facial gestures is a technology that aims to improve product and services performance by monitoring customer behavior to specific products or service staff by their evaluation. In the past few years there have been a wide variety of advances performed in terms of feature extraction mechanisms , detection of face and also expression classification techniques.This paper is the implementation of an Ensemble CNN for building a real-time system that can detect emotion and gender of the person.The Experimental results shows accuracy of 68% for Emotion classification into 7 classes (angry, fear, sad, happy,surprise, neutral, disgust) on FER-2013 dataset and 95% for Gender Classification (Male or Female) on IMDB dataset.Our work can predict Emotion and Gender on single face images as well as multiple face images. Also when input is given through webcam our complete pipeline of this real-time system can take less than 0.5 seconds to generate results.

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