Emotion recognition from face dataset using deep neural nets

Das, Deepjoy; Chakrabarty, Alok

doi:10.1109/inista.2016.7571861

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…MachineLearning Viola-Jones, PCA and SIFT [2] Using smart secure systems for door lock and unlocking came popular currently. This system provides either a facial recognition security character or a keypad is delivered to enter the pass code to unlock the door.…”

Section: Methods/ Algorithmmentioning

confidence: 99%

Face recognition based banking system using machine learning

Mohanraj¹,

Ramya²,

Sandhiya³

2022

ijhs

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The main ideal of this design is to recognize human face and extracting corresponding bank details to bank account users. It helps people to know about their regular transactions and it saves more time in banks. Face is the pivotal part of the human body that uniquely identifies a person. Using the face characteristics as biometric, the face recognition system can be enforced. The most demanding task in all banks is facing unwanted crowds for simple conditions. In traditional banking system, the bank users are called out by the bank employee commemorative wise to know the details of bank users. By enter the name or account number of the user they come to know about the full details of corresponding user. It takes further time for both users and workers. This model integrates a camera that captures an input image, an algorithm for detecting face from an input image, encoding and identifying the face, delivering the banking details directly to the bank users.

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Section: Methods/ Algorithmmentioning

confidence: 99%

Face recognition based banking system using machine learning

Mohanraj¹,

Ramya²,

Sandhiya³

2022

ijhs

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“…Delving in the task of detecting emotional states, facial expressions have become a popular choice due to their universality and intrinsic relation to emotions [12]. By means of conventional cameras and in a controlled environment, the datasets CMU [13] [14] and FER-2013 [15] collected static images of facial expressions from participants who were requested to act different emotional states following the discrete emotions scheme [16]. Aiming to simplify the collection of static images and to reach a greater number of participants, the authors of the Gamo dataset [17] made use of a web-based interface were participants play a game by performing specific facial expressions captured by a web camera.…”

Section: Affective Datasetsmentioning

confidence: 99%

Emotion estimation in crowds: a survey

Urizar

Barakova

Regazzoni

et al. 2017

8th International Conference of Pattern Recognition Systems (ICPRS 2017)

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Emotions play an important role in human behavior, even more so in large congregations of people where emotional states are prompt to be contaged and amplified. This work presents a qualitative systematic review of the literature concerning the estimation of emotions and affects in real-life crowded environments, covering the aspects of methods and datasets. The academic search engine Scopus was inquired and the search was limited to publications in the English language addressing any of the aforementioned aspects. The aim of this contribution is to highlight advances, limitation and trends in addressing the estimation of emotions in crowds.

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“…But here we took Total Variation (TV) as a method which is better than anisotropic filtering [5]. For detecting and classifying the features in a MRI data many techniques are used like SVM (Support Vector Machine), k-NN (k-Nearest Neighbour) [6], NN (Neural Networks) [7], Deep Learning Based CNN (Convolutional Neural Network) [8], [9] etc. Before applying classification techniques, pre-processing techniques should be applied.…”

Section: Introductionmentioning

confidence: 99%

Analysis on Methods for Detecting Brain Tumor from MRI images

Kumar¹,

Peter²,

Kingsly³

2022

IJRASET

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Brain tumor detection from MRI images is a challenging process due to high diversity in the tumor pixels of different peoples. Automatic detection has got wide spread acclaim because the manual detection by experts is time consuming and prone to error in judjement.Due to its high mortality rate, detection of tumor automatically is a new emerging technique in bio medical imaging. Here we present a review of few methods from simple threholding to advanced deep learning methods for segmentation of tumor from MRI data. The segmentation of tumor methods is classified to image segmentation using gray level processing, machine learning and deep learning. We compare the result of various method in an attempt to find the best methods available. As medical imaging methods have improving day by day we hope this review will help to understand emerging trends in brain tumor detection.

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Emotion recognition from face dataset using deep neural nets

Cited by 14 publications

References 14 publications

Face recognition based banking system using machine learning

Face recognition based banking system using machine learning

Emotion estimation in crowds: a survey

Analysis on Methods for Detecting Brain Tumor from MRI images

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