A Comparative Study Between Deep Learning and Traditional Machine Learning Techniques for Facial Biometric Recognition

Finizola, Jonnathann Silva; Targino, Jonas Mendonça; Teodoro, Felipe Gustavo Silva; Lima, Clodoaldo A. M.

doi:10.1007/978-3-030-03928-8_18

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Furthermore, it leads to a five times larger dataset compared to five-second windows, which can be beneficial for deep learning models. These windows are directly used to train the deep learning models as they can learn features from raw data [ 84 ]. We stack the windows of the six axes (three for each sensor) above each other, resulting in a

matrix, used as the input for the deep learning models.…”

Section: Methodsmentioning

confidence: 99%

HARTH: A Human Activity Recognition Dataset for Machine Learning

Logacjov

Bach

Kongsvold

et al. 2021

Sensors

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Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.

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matrix, used as the input for the deep learning models.…”

Section: Methodsmentioning

confidence: 99%

HARTH: A Human Activity Recognition Dataset for Machine Learning

Logacjov

Bach

Kongsvold

et al. 2021

Sensors

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“…It is therefore important to find solutions to improve these processes to ensure a smooth and secure travel experience for all. Some of the recent methods used for passport control at airports include the use of passport scanners, facial recognition cameras, biometric technologies such as iris and fingerprint recognition 14, based identity control systems 15 . These methods allow for quick and accurate identification of travellers, thus improving the efficiency and security of passport control processes.…”

Section: The Problem Of This Studymentioning

confidence: 99%

Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model

et al. 2023

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The use of real-time machine learning to optimize passport control procedures at airports can greatly improve both the efficiency and security of the processes. To automate and optimize these procedures, AI algorithms such as character recognition, facial recognition, predictive algorithms and automatic data processing can be implemented. The proposed method is to use the R-CNN object detection model to detect passport objects in real-time images collected by passport control cameras. This paper describes the step-by-step process of the proposed approach, which includes pre-processing, training and testing the R-CNN model, integrating it into the passport control system, and evaluating its accuracy and speed for efficient passenger flow management at international airports. The implementation of this method has shown superior performance to previous methods in terms of reducing errors, delays and associated costs

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“…Introducing DL-based models for detecting cyberbullying over traditional models has several benefits. When the data size is large, several studies [11][12][13][14] have shown that DL algorithms outperform the traditional ML algorithms. Extracting features manually for text and image classification is a tedious and error-prone task.…”

Section: Introductionmentioning

confidence: 99%

A Review on Deep-Learning-Based Cyberbullying Detection

et al. 2023

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Bullying is described as an undesirable behavior by others that harms an individual physically, mentally, or socially. Cyberbullying is a virtual form (e.g., textual or image) of bullying or harassment, also known as online bullying. Cyberbullying detection is a pressing need in today’s world, as the prevalence of cyberbullying is continually growing, resulting in mental health issues. Conventional machine learning models were previously used to identify cyberbullying. However, current research demonstrates that deep learning surpasses traditional machine learning algorithms in identifying cyberbullying for several reasons, including handling extensive data, efficiently classifying text and images, extracting features automatically through hidden layers, and many others. This paper reviews the existing surveys and identifies the gaps in those studies. We also present a deep-learning-based defense ecosystem for cyberbullying detection, including data representation techniques and different deep-learning-based models and frameworks. We have critically analyzed the existing DL-based cyberbullying detection techniques and identified their significant contributions and the future research directions they have presented. We have also summarized the datasets being used, including the DL architecture being used and the tasks that are accomplished for each dataset. Finally, several challenges faced by the existing researchers and the open issues to be addressed in the future have been presented.

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A Comparative Study Between Deep Learning and Traditional Machine Learning Techniques for Facial Biometric Recognition

Cited by 4 publications

References 13 publications

HARTH: A Human Activity Recognition Dataset for Machine Learning

HARTH: A Human Activity Recognition Dataset for Machine Learning

Improving the efficiency and security of passport control processes at airports by using the R-CNN object detection model

A Review on Deep-Learning-Based Cyberbullying Detection

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