Systematic evaluation of deep face recognition methods

You, Mingyu; Han, Xuan; Xu, Yangliu; Li, Li

doi:10.1016/j.neucom.2020.01.023

Cited by 19 publications

(8 citation statements)

References 12 publications

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“…Specifically, the conventional FER of sLDA and multi-SVM has significant shortcomings in real time, accuracy, and robustness. Many researchers have used CNN algorithms for FER (Liong Zhang et al 2019;You et al 2020), enabling computers to read meanings expressed in face images more quickly and accurately. Dense-Net121, Res Net50, VGG16, and VGG19Net are FER methods based on deep learning that can solve the shortcomings of conventional expression recognition methods.…”

Section: Network Trainingmentioning

confidence: 99%

A method for evaluating the learning concentration in head-mounted virtual reality interaction

2022

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In education, learning concentration is closely related to the quality of learning, and teachers can adjust their teaching methods accordingly to improve the learning outcomes of students. Particularly in head-mounted virtual reality interactions, current methods for assessing learning concentration cannot be fully applied to new interactive environments because immersion shaping and cognitive formation differ from the conventional education. Therefore, in this study, a learning concentration assessment method is proposed to measure the learning concentration of students in head-mounted virtual interaction, using the expression score, visual focus rate, and task mastery as evaluation indicators. In addition, the weights of the evaluation indicators can be configured to be included in the calculation of learning concentration depending on the characteristics of different types of courses. The results of a usability evaluation indicate that the learning concentration of students can be effectively evaluated using the proposed method. By developing and implementing strategies for optimizing learning effects, the learning concentration and assessment scores of students increased by 18% and 15.39%, respectively.

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Section: Network Trainingmentioning

confidence: 99%

A method for evaluating the learning concentration in head-mounted virtual reality interaction

2022

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“…Developing different deep FR methods and their deployment in real-world applications requires a systematic performance evaluation. Iandola et al [65] provided an evaluation framework for different datasets and SOTA methods. They used the following criteria: data augmentation, network architecture, loss function, training strategy, and model compression.…”

Section: Some Current Research In DL For Frmentioning

confidence: 99%

Learning Representations for Face Recognition: A Review from Holistic to Deep Learning

Barreto

Sarvaiya

Patnaik³

2022

Adv. technol. innov.

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For decades, researchers have investigated how to recognize facial images. This study reviews the development of different face recognition (FR) methods, namely, holistic learning, handcrafted local feature learning, shallow learning, and deep learning (DL). With the development of methods, the accuracy of recognizing faces in the labeled faces in the wild (LFW) database has been increased. The accuracy of holistic learning is 60%, that of handcrafted local feature learning increases to 70%, and that of shallow learning is 86%. Finally, DL achieves human-level performance (97% accuracy). This enhanced accuracy is caused by large datasets and graphics processing units (GPUs) with massively parallel processing capabilities. Furthermore, FR challenges and current research studies are discussed to understand future research directions. The results of this study show that presently the database of labeled faces in the wild has reached 99.85% accuracy.

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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%

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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Systematic evaluation of deep face recognition methods

Cited by 19 publications

References 12 publications

A method for evaluating the learning concentration in head-mounted virtual reality interaction

A method for evaluating the learning concentration in head-mounted virtual reality interaction

Learning Representations for Face Recognition: A Review from Holistic to Deep Learning

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

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