Robust Face Recognition Technique under Varying Illumination

Shah, Jamal Hussain; Sharif, Muhammad; Raza, Mudassar; Murtaza, Marryam; Ur-Rehman, Saeed

doi:10.1016/s1665-6423(15)30008-0

Cited by 26 publications

(8 citation statements)

References 25 publications

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“…Specifically, on face samples with lighting variation, techniques were proposed that compliment both the traditional and deep learning FR methods reporting improved performance [26,15]. The approaches include pre-processing of the facial samples to normalise any variation, before feeding it to the face recognition algorithm [33,17,50,34,35].…”

Section: Related Workmentioning

confidence: 99%

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

Varkarakis,

Yao,

Corcoran

2021

Preprint

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The recent availability of low-power neural accelerator hardware, combined with improvements in end-to-end neural facial recognition algorithms provides enabling technology for on-device facial authentication. The present research work examines the effects of directional lighting on a State-of-Art (SoA) neural face recognizer. A synthetic re-lighting technique is used to augment data samples due to the lack of public data-sets with sufficient directional lighting variations. Top lighting and its variants (top-left, topright) are found to have minimal effect on accuracy, while bottom-left or bottom-right directional lighting have the most pronounced effects. Following the fine-tuning of network weights, the face recognition model is shown to achieve close to the original Receiver Operating Characteristic curve (ROC) performance across all lighting conditions, and demonstrates an ability to generalize beyond the lighting augmentations used in the fine-tuning dataset. This work shows that a SoA neural face recognition models can be tuned to compensate for directional lighting effects, removing the need for a pre-processing step prior to applying facial recognition.

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Section: Related Workmentioning

confidence: 99%

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

Varkarakis,

Yao,

Corcoran

2021

Preprint

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“…The recognition of a person from the face is a famous method used for authentication [18,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. It is easy to get data of an individual for his/her facial features rather than putting a palm on the scanner or placing an eye in front of a retina scanner for a retina scan.…”

Section: Face Recognitionmentioning

confidence: 99%

“…The recognition through the face is used for various authentication purposes such as national ID card, live investigation, passport, and so forth. In the process of face recognition, pre-processing is carried out that includes the standardization [26,39,[49][50][51][52][53][54][55], resizing of the image [35,56], and identification [25,32,43,44,50,55,57]. The image is retrieved from the database and used for perceiving before the pre-processing.…”

Section: Face Recognitionmentioning

confidence: 99%

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A Survey on Various Unimodal Biometric Techniques

Mehmood¹,

Mishra²

2021

STAIQC

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The agriculture plays very important role as it helps to accomplish the need of food among people. The production in agriculture consequentially contributes to the economy of every country. The grain crops rice, wheat, maize, and legumes are suffering a lot due to some viral, bacterial, and fungal diseases. The pest and variety of diseases can bring a heavy loss to the global economy. The monitoring of crops health and identification of diseases at early days is very challenging and emerging task in agriculture. So, it is very important to prevent crops from fatal diseases in the early stage, but the manual process of disease discovery can lead to erroneous magnitude of pesticides. The trouble is figure out by automate discovery of diseases and supplication of relevant medication on time. It is very necessary to find out accurate disease to overcome heavy loss to economy. From the few decades, to detect disease correctly, the process of detection become automate using emerging technologies and techniques using computer vision, machine learning and image processing. This article presents the extensive literature on existing methodologies utilized for recognition and classification of leaves diseases. The studies addresses that there is still many limitations and challenges find in different phases in plant disease detection system. The presented research also highlights the pros and cons of different techniques that help out the researchers for contribution in future.

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“…In their research article [20], authors have proposed a system for robust facial recognition technique under varying illumination conditions. In this research article, authors have concentrated on the problems concerning to the textural based illumination handling for the face recognition under both indoor and outdoor lighting conditions.…”

Section: A Comprehensive Review On Various Face Recognition System Usmentioning

confidence: 99%

A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

Chokkadi¹,

Sudeepa

et al. 2019

IJATCSE

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Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification have evolved enormously over the past few decades. Sketch recognition is one of the most important areas that have evolved as an integral component adopted by the agencies of law administration in current trends of forensic science. Matching of derived sketches to photo images of face is also a difficult assignment as the considered sketches are produced upon the verbal explanation depicted by the eye witness of the crime scene and may have scarcity of sensitive elements that exist in the photograph as one can accurately depict due to the natural human error. Substantial amount of the novel research work carried out in this area up late used recognition system through traditional extraction and classification models. But very recently, few researches work focused on using deep learning techniques to take an advantage of learning models for the feature extraction and classification to rule out potential domain challenges. The first part of this review paper basically focuses on deep learning techniques used in face recognition and matching which as improved the accuracy of face recognition technique with training of huge sets of data. This paper also includes a survey on different techniques used to match composite sketches to human images which includes component-based representation approach, automatic composite sketch recognition technique etc.

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Robust Face Recognition Technique under Varying Illumination

Cited by 26 publications

References 25 publications

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

Towards End-to-End Neural Face Authentication in the Wild -- Quantifying and Compensating for Directional Lighting Effects

A Survey on Various Unimodal Biometric Techniques

A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

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