A robust deep learning approach for glasses detection in non‐standard facial images

Bekhet, Saddam; Alahmer, Hussein

doi:10.1049/bme2.12004

Cited by 12 publications

(9 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNNs is a breakthrough advance in artificial intelligence that changed the entire related CV research [1], [32]- [34]. These CNNs use comparatively less pre-processing than other methods that utilizes feature extraction and description.…”

Section: Reserch Methodsmentioning

confidence: 99%

“…Automated soft biometrics identification has attracted a great deal of attention in the past era. This was primarily related to the uprising dependence on surveillance systems that produces enormous volumes of data that need to be examined [1], [2] in an off-line manner. Furthermore, it is possible to obtain soft biometrics without subject participation from low quality videos/images, making them highly valuable [2].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it is possible to obtain soft biometrics without subject participation from low quality videos/images, making them highly valuable [2]. In addition, people normally utilize these soft biometric to identify each other [1]. Finally, in today's social era, images are described digitally while vast users are interested in their semantic content.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gender recognition from unconstrained selfie images: a convolutional neural network approach

Bekhet

Alghamdi

Taj-Eddin

2022

IJECE

Self Cite

View full text Add to dashboard Cite

Human gender recognition is an essential demographic tool. This is reflected in forensic science, surveillance systems and targeted marketing applications. This research was always driven using standard face images and hand-crafted features. Such way has achieved good results, however, the reliability of the facial images had a great effect on the robustness of extracted features, where any small change in the query facial image could change the results. Nevertheless, the performance of current techniques in unconstrained environments is still inefficient, especially when contrasted against recent breakthroughs in different computer vision research. This paper introduces a novel technique for human gender recognition from non-standard selfie images using deep learning approaches. Selfie photos are uncontrolled partial or full-frontal body images that are usually taken by people themselves in real-life environment. As far as we know this is the first paper of its kind to identify gender from selfie photos, using deep learning approach. The experimental results on the selfie dataset emphasizes the proposed technique effectiveness in recognizing gender from such images with 89% accuracy. The performance is further consolidated by testing on numerous benchmark datasets that are widely used in the field, namely: Adience, LFW, FERET, NIVE, Caltech WebFaces and CAS-PEAL-R1.

show abstract

Section: Reserch Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Gender recognition from unconstrained selfie images: a convolutional neural network approach

Bekhet

Alghamdi

Taj-Eddin

2022

IJECE

Self Cite

View full text Add to dashboard Cite

show abstract

“…The field of computer vision witnessed an unprecedented use of CNNs [26], especially for video/image analysis [26]. CNNs requires less pre-processing effort, this is in contrast with other different feature extraction and classification algorithms.…”

Section: The Proposed Methodsmentioning

confidence: 99%

An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

Bekhet¹,

Alkinani²,

Tabares-Soto³

et al. 2021

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and monetary. The situation is very complex as the COVID-19 test kits are limited, therefore, more diagnostic methods must be developed urgently. A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography (CT), where any chest anomalies (e.g., lung inflammation) can be easily identified. Most hospitals possess X-ray or CT imaging equipments that can be used for early detection of COVID-19. Motivated by this, various artificial intelligence (AI) techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT images. However, the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs, which is not widely available in several countries. This paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks (CNNs), which does not require a custom hardware to run compared to currently available CNN models. The proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU (0.54% of AlexNet parameters). This model is highly beneficial for countries where high-end GPUs are luxuries. Experimental outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96% accuracy.

show abstract

“…Bekhet and Alahmer [11] developed a deep learning-based recognition model for detection of glasses on a realistic selfie dataset which includes challenging and non-synthesized images of full or partial faces. After an extensive training of almost two weeks on a CNN based transfer learning model, they were able to such a model with an accuracy score of 96% on such a varied dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-time eyeglass detection using transfer learning for non-standard facial data

Jain

Goyal

Kalaichelvi

2022

IJECE

View full text Add to dashboard Cite

The aim of this paper is to build a real-time eyeglass detection framework based on deep features present in facial or ocular images, which serve as a prime factor in forensics analysis, authentication systems and many more. Generally, eyeglass detection methods were executed using cleaned and fine-tuned facial datasets; it resulted in a well-developed model, but the slightest deviation could affect the performance of the model giving poor results on real-time non-standard facial images. Therefore, a robust model is introduced which is trained on custom non-standard facial data. An Inception V3 architecture based pre-trained convolutional neural network (CNN) is used and fine-tuned using model hyper-parameters to achieve a high accuracy and good precision on non-standard facial images in real-time. This resulted in an accuracy score of about 99.2% and 99.9% for training and testing datasets respectively in less amount of time thereby showing the robustness of the model in all conditions.

show abstract

A robust deep learning approach for glasses detection in non‐standard facial images

Cited by 12 publications

References 41 publications

Gender recognition from unconstrained selfie images: a convolutional neural network approach

Gender recognition from unconstrained selfie images: a convolutional neural network approach

An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

Real-time eyeglass detection using transfer learning for non-standard facial data

Contact Info

Product

Resources

About