An Automated System in ATM Booth Using Face Encoding and Emotion Recognition Process

Chowdhury, Atiqul Islam; Shahriar, Mohammad Munem; Islam, Ashraful; Ahmed, Eshtiak; Karim, Asif; Islam, Mohammad Rezwanul

doi:10.1145/3421558.3421567

Cited by 17 publications

(6 citation statements)

References 6 publications

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“…The proposed novel ternary biometrics fusion with current ATM methods proved more promising authentication results using the Prewitt filter, Cellular Automata Segmentation, and DWT Mexican Hat Wavelet transform. Our proposed system has succeeded in identifying authorized person's biometric images based on Specificity, Sensitivity Precision, Accuracy, PSNR, and SSIM observation readings, where the accuracy match reached 98.5%, which is the highest rate compared to most of the previous works [3,29].…”

Section: Discussionmentioning

confidence: 75%

“…If someone enters an incorrect code, that person's face is to be saved and emailed to help identify criminals. Chowdhury et al in paper [3] have presented the work based on multiple users' emotions. They have also used the CNN (Convolutional Neural Network) model to implement a facial recognition library and validated results based on feelings of "Happiness."…”

Section: Literature Reviewmentioning

confidence: 99%

“…13A respectively. [3] Face only 67% Method [4] Face only 95% Method [5] Face & Fingerprints only 70% Method [6] Face only 76%, 96% Method [7] Face only 72.24% Method [28] Face only 96% Method [29] Finger only 94%…”

Section: Cnn Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Automated Teller Machine Authentication Using Biometric

Aljuaid¹,

Ansari²

2022

Computer Systems Science and Engineering

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Section: Discussionmentioning

confidence: 75%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Automated Teller Machine Authentication Using Biometric

Aljuaid¹,

Ansari²

2022

Computer Systems Science and Engineering

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“…The accuracy of this study has been shown to be 82.0% for both classes of valence and arousal [ 19 ]. To classify emotions based on their valence and arousal, machine learning models [ 20 ] such as bagging trees (BT), support vector machines (SVM), linear discriminant analysis (LDA), Bayesian linear discriminant analysis (BLDA) models, and deep convolutional neural networks (CNN) are used. Deep CNN achieved the best recognition performance on features that combined temporal and frequency information [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network

Aung

Hassan²,

Brady

et al. 2022

Computational Intelligence and Neuroscience

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Humans experience a variety of emotions throughout the course of their daily lives, including happiness, sadness, and rage. As a result, an effective emotion identification system is essential for electroencephalography (EEG) data to accurately reflect emotion in real-time. Although recent studies on this problem can provide acceptable performance measures, it is still not adequate for the implementation of a complete emotion recognition system. In this research work, we propose a new approach for an emotion recognition system, using multichannel EEG calculation with our developed entropy known as multivariate multiscale modified-distribution entropy (MM-mDistEn) which is combined with a model based on an artificial neural network (ANN) to attain a better outcome over existing methods. The proposed system has been tested with two different datasets and achieved better accuracy than existing methods. For the GAMEEMO dataset, we achieved an average accuracy ± standard deviation of 95.73% ± 0.67 for valence and 96.78% ± 0.25 for arousal. Moreover, the average accuracy percentage for the DEAP dataset reached 92.57% ± 1.51 in valence and 80.23% ± 1.83 in arousal.

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“…Basically, they used a deep learning tool 'The Convolutional Neural network' to recognize the plate character and they also used thresholding to extract the plate region. A paper used deep object detection convolutional neural network [7] and another one used Local binary pattern (LBP), histogram matching technique and matching bounding box technique [8].…”

Section: Related Workmentioning

confidence: 99%

An Automated Approach for the Recognition of Bengali License Plates

Nasim¹,

Chowdhury²,

Muna³

et al. 2021

Preprint

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Automatic Number Plate Recognition (ALPR) is a system for automatically identifying the license plates of any vehicle. This process is important for tracking, ticketing, and any billing system, among other things. With the use of information and communication technology (ICT), all systems are being automated, including the vehicle tracking system. This study proposes a hybrid method for detecting license plates using characters from them. Our captured image information was used for the recognition procedure in Bangladeshi vehicles, which is the topic of this study. Here, for license plate detection, the YOLO model was used where 81% was correctly predicted. And then, for license plate segmentation, Otsu's Thresholding was used and eventually, for character recognition, the CNN model was applied. This model will allow the vehicle's automated license plate detection system to avoid any misuse of it.

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An Automated System in ATM Booth Using Face Encoding and Emotion Recognition Process

Cited by 17 publications

References 6 publications

Automated Teller Machine Authentication Using Biometric

Automated Teller Machine Authentication Using Biometric

Entropy-Based Emotion Recognition from Multichannel EEG Signals Using Artificial Neural Network

An Automated Approach for the Recognition of Bengali License Plates

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