Facial Recognition Using Deep Learning

Singh, Nidhi R; Hariharan, S; Gupta, Manika

doi:10.1007/978-981-15-0372-6_30

Cited by 13 publications

(16 citation statements)

References 6 publications

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“…Research on machine learning techniques in many diverse applications, some of them safety-critical, has proliferated in recent years [26][27][28][29][30]. Concerns about machine learning in safety-critical systems have led to research to develop techniques for safety and/or explainability of ML components [32][33][34].…”

Section: Related Workmentioning

confidence: 99%

Impact of Machine Learning on Safety Monitors

Terrosi

Strigini²,

Bondavalli³

2022

Lecture Notes in Computer Science

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Machine Learning components in safety-critical applications can perform some complex tasks that would be unfeasible otherwise. However, they are also a weak point concerning safety assurance. An aspect requiring study is how the interactions between machine-learning components and other non-ML components evolve with training of the former. It is theoretically possible that learning by Neural Networks may reduce the effectiveness of error checkers or safety monitors, creating a major complication for safety assurance. We present an initial exploration of this problem focused on automated driving, where machine learning is heavily used. We simulated operational testing of a standard vehicle architecture, where a machine learning-based Controller is responsible for driving the vehicle and a separate Safety Monitor is provided to detect hazardous situations and trigger emergency action to avoid accidents. Among the results, we observed that indeed improving the Controller could make the Safety Monitor less effective; it is even possible for a training increment to make the Controller's own behaviour safer but the vehicle's less safe. We discuss implications for practice and for research.

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Section: Related Workmentioning

confidence: 99%

Impact of Machine Learning on Safety Monitors

Terrosi

Strigini²,

Bondavalli³

2022

Lecture Notes in Computer Science

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“…Recent advances in deep learning methods have contributed to significant performance improvements in a wide range of computer vision applications. They have been particularly successful for face detection problems where modern deep CNN models show a significant accuracy improvement in comparison to traditional approaches based on hand-crafted features [22][23][24][25][26][27][28][29][39][40][41][42][43][44][45]. Consequently, these deep learning methods have become the state-of-the-art for face detection.…”

Section: Related Workmentioning

confidence: 99%

“…Nowadays, deep learning approaches are widely applied for face detection as they enable the system to automatically learn representations from raw input images using a Convolutional Neural Network (CNN), achieving a high accuracy under very challenging detection conditions [22][23][24][25][26][27][28][29]. Most of the deep-learning-based face detectors are, however, computationally demanding and may not be suitable for applications that analyze large amounts of data and require real-time performance, such as the CSEM detection systems in forensic tools.…”

Section: Introductionmentioning

confidence: 99%

“…Usually, traditional [ 17 , 18 , 19 , 20 , 21 ] and deep-learning-based face detectors [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ] are designed to process 2D images, but they have also been extended to analyze 3D data [ 30 , 31 , 32 , 33 ]. These approaches take advantage of deep information to be less sensitive to low/high illumination conditions and viewpoint variation in comparison to 2D face detectors.…”

Section: Introductionmentioning

confidence: 99%

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Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Chaves

Fidalgo

Alegre

et al. 2020

Sensors

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Face recognition is a valuable forensic tool for criminal investigators since it certainly helps in identifying individuals in scenarios of criminal activity like fugitives or child sexual abuse. It is, however, a very challenging task as it must be able to handle low-quality images of real world settings and fulfill real time requirements. Deep learning approaches for face detection have proven to be very successful but they require large computation power and processing time. In this work, we evaluate the speed–accuracy tradeoff of three popular deep-learning-based face detectors on the WIDER Face and UFDD data sets in several CPUs and GPUs. We also develop a regression model capable to estimate the performance, both in terms of processing time and accuracy. We expect this to become a very useful tool for the end user in forensic laboratories in order to estimate the performance for different face detection options. Experimental results showed that the best speed–accuracy tradeoff is achieved with images resized to 50% of the original size in GPUs and images resized to 25% of the original size in CPUs. Moreover, performance can be estimated using multiple linear regression models with a Mean Absolute Error (MAE) of 0.113, which is very promising for the forensic field.

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“…Deep learning has become the predominant solution for most facial analysis problems. The preference for deep learning models in the computer vision field is due to the robustness of architectures such as convolutional neural networks or residual connections, which effectively extracts all the necessary facial features without manually engineering the features using several filters [1,2]. Gender estimation is the idea of training a machine learning model on thousands of labelled facial images to produce a function that maps an input image X to a corresponding label Y.…”

Section: Introductionmentioning

confidence: 99%

Artificially Generated Facial Images for Gender Classification Using Deep Learning

Raman¹,

ELKarazle²,

Then³

2023

Computer Systems Science and Engineering

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Given the current expansion of the computer vision field, several applications that rely on extracting biometric information like facial gender for access control, security or marketing purposes are becoming more common. A typical gender classifier requires many training samples to learn as many distinguishable features as possible. However, collecting facial images from individuals is usually a sensitive task, and it might violate either an individual's privacy or a specific data privacy law. In order to bridge the gap between privacy and the need for many facial images for deep learning training, an artificially generated dataset of facial images is proposed. We acquire a pre-trained Style-Generative Adversarial Networks (StyleGAN) generator and use it to create a dataset of facial images. We label the images according to the observed gender using a set of criteria that differentiate the facial features of males and females apart. We use this manuallylabelled dataset to train three facial gender classifiers, a custom-designed network, and two pre-trained networks based on the Visual Geometry Group designs (VGG16) and (VGG19). We cross-validate these three classifiers on two separate datasets containing labelled images of actual subjects. For testing, we use the UTKFace and the Kaggle gender dataset. Our experimental results suggest that using a set of artificial images for training produces a comparable performance with accuracies similar to existing state-of-the-art methods, which uses actual images of individuals. The average classification accuracy of each classifier is between 94% and 95%, which is similar to existing proposed methods.

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Facial Recognition Using Deep Learning

Cited by 13 publications

References 6 publications

Impact of Machine Learning on Safety Monitors

Impact of Machine Learning on Safety Monitors

Assessment and Estimation of Face Detection Performance Based on Deep Learning for Forensic Applications

Artificially Generated Facial Images for Gender Classification Using Deep Learning

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