Face detection and recognition with video database

Yadhul, Kolcharam; Mary, I. Thusnavis Bella; Lakshmi, P.; Saju, Ansa

doi:10.1109/ecs.2014.6892662

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…To learn blur-robust face representations the authors artificially blur training data composed of clear still images to account for a shortfall in real-world video training data. Finally, (Yadhul et al, 2014) reduce the computational complexity in face detection and improve the accuracy rate of face recognition in video datasets. The Viola-Jones algorithm is used for face detection.…”

Section: 60%mentioning

confidence: 99%

“…To get a clearer picture in Table 13 we present the achieved accuracies of all 150 analyzed papers. (Schroff et al, 2015) LFW 99.63% (Schroff et al, 2015) YTF 95.12% (Taigman et al, 2015) LFW 95.17% (Syafeeza et al, 2015) ORL 100.00% (Jalali et al, 2015) FERET 88.80%-92.00% (Huang and Yuan, 2015) FERET 97.40% (Sun et al, 2015a) LFW 99.47% (Sun et al, 2015a) YTF 93.20% (Yadhul et al, 2014) YTCD 97.09% (Zhang et al, 2016) ORL 93.33% (Simón et al, 2016) RGB-D-T face / (Sun et al, 2013) LFW 93.83% (Peng et al, 2016) CASIA NIR 96.02%-100.00% (Herrmann et al, 2016) YTF 80.30% (Li and Zhu, 2016) YTF 94.56%-98.43% (Vizilter et al, 2016) LFW 96.30%-98.59% (Zheng et al, 2016) CASIA-WebFace 96.40%-97.90% (Guo et al, 2016a) ORL 97.50% (Singh and Om, 2017) IIT(BHU) 86.22%-91.03% (Guo et al, 2017) SunWin Face 80.34%-99.26% (Guo et al, 2017) LFW 98.95% (Guo et al, 2017) HIT LAB2 89.80%-98.74% (Guo et al, 2017) YTF 97.30% (Hu et al, 2017) AT&T 91.25%-95.00% (Hu et al, 2017) FRGCv2.0 85.15% (Fu et al, 2017) LFW 84.10%-97.10% (Bukovčiková et al, 2017) CelebA 85.74% (Liu et al, 2017 (Zeng et al, 2017) AR face Set 1 88.30%-100.00% (Zeng et al, 2017) AR face Set 2 76.30%-99.80% (Gruber et al, 2017) Casia-WebFace 90.70%-91.50% (Yeung et al, 2017) LFW 83.40% (Kim et al, 2017) custom dataset 96.70% (Reale et al, 2017) CASIA HFB 99.52% (Reale et al, 2017) CASIA NIR-VIS 2.0.…”

Section: Assessment Of Q5: What Is the Accuracy Of Cnns For Facial Re...mentioning

confidence: 99%

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Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

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This paper presents a comprehensive overview of Convolutional Neural Networks (CNNs) in the context of face recognition. By analyzing 150 research papers, we investigate major publication channels, temporal trends, prevalent CNN models, primary data sets, accuracy levels, primary research focuses, and future prospects for improving CNN-based facial recognition. A major focus is placed on identifying prevalent CNN architectures, techniques used for facial recognition and shedding light on the evolving landscape of CNN designs. Furthermore, we examine the datasets used for training and testing CNNs, and evaluate the accuracy levels achieved by these models. Lastly, we discuss future directions for enhancing CNN-based facial recognition, including addressing bias and fairness, improving robustness to environmental variations, privacy preservation, and exploring transfer learning and multimodal fusion. This paper serves as a valuable resource, summarizing major trends in CNN-based face recognition. It provides insights for researchers and practitioners, guiding future advancements in this rapidly evolving field.

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Section: 60%mentioning

confidence: 99%

Section: Assessment Of Q5: What Is the Accuracy Of Cnns For Facial Re...mentioning

confidence: 99%

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Barcic¹,

Grd²,

Tomičić³

2023

Preprint

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“…The work requires initialization the algorithms by inputting initial estimates of the scene. [5] introduced the possibility of using the image sensor as luminance sensor. The illumination level determined the amount of lighting needed to maintain the contrast in the overall view of objects in the shop window under external natural illumination.…”

Section: Prelated Workmentioning

confidence: 99%

Development and Validation of a Camera-Based Illumination Controller for People Detection, Tracking and Recognition using Computational Intelligence

Llorente¹,

Dadios²

2019

IJRTE

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Illumination is crucial in human activities and in machine vision applications. For indoor surveillance applications, Infrared (IR) Light Emitting Diodes (LEDs) are the common means of providing illumination to the camera to cause no discomfort to human occupants. While IR provides non-obtrusive illumination for the camera, the same energy consumed does not provide the illumination to indoor spaces of the building. This is important if the premises where the camera is installed is not connected to the main power source or electric grid but derives energy from renewable sources. In this work, an illumination controller based on fuzzy logic system is developed and integrated to a vision system and an LED lighting system to provide a constant level of illumination to an object regardless of its distance from the image sensor. The computer vision system performs human object detection and face recognition and outputs fuzzy values representing the inferred distance of detected objects where the fuzzy system generates crisp output of duty cycle settings for the PWM controller for the LED lighting system to provide the required illumination needed by the vision system. Optimum illumination level for the vision system to perform the detection, tracking and face recognition operations must be provided by the system. Using visible Light Emitting Diodes as source of illumination, the system provides illumination both for the proper operation of the camera and human personnel monitoring the premises where the system is installed. This feature is significant in energy-constrained surveillance applications or where there is no power source derived from the electric grid.

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Proposed System for Criminal Detection and Recognition on CCTV Data Using Cloud and Machine Learning

Shirsat¹,

Naik²,

Tamse³

et al. 2019

2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN)

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Face detection and recognition with video database

Cited by 3 publications

References 12 publications

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Convolutional Neural Networks for Face Recognition: A Systematic Literature Review

Development and Validation of a Camera-Based Illumination Controller for People Detection, Tracking and Recognition using Computational Intelligence

Proposed System for Criminal Detection and Recognition on CCTV Data Using Cloud and Machine Learning

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