Designing Face Detection Systems with Gray Wolf Optimization.
Saved in:
| Title: | Designing Face Detection Systems with Gray Wolf Optimization. |
|---|---|
| Authors: | Sabah Abbod, Noor1 noorsabah@uomustansiriyah.edu.iq, Mohasefi, Jamshid B.2 |
| Source: | Iraqi Journal for Electrical & Electronic Engineering. Jun2026, Vol. 22 Issue 1, p233-244. 12p. |
| Subjects: | Grey Wolf Optimizer algorithm, Metaheuristic algorithms, Human facial recognition software, Machine learning, Deep learning, Artificial neural networks, Convolutional neural networks, Computer vision |
| Abstract (English): | The main objective of this paper project was to create a state-of-the-art face identification technique that can handle the various difficulties caused by changes in illumination, occlusions, and facial emotions. Face detection is a cornerstone of computer vision, facilitating diverse applications ranging from surveillance systems to human-computer interaction. Throughout this paper, the comprehensive exploration of advancing face detection methodologies has been undertaken, culminating in developing and evaluating a novel approach. The challenges posed by variations in facial expressions, lighting conditions, and occlusions necessitated a multifaceted solution. Our proposed method, which consists of interconnected steps, works quite well to overcome these challenges. Using deep learning architectures to increase feature extraction and discrimination was beneficial in the initial stage of fine-tuning Residual Networks (ResNet-50) to serve as the Region-based Convolutional Neural Network (Faster R-CNN) framework classifier. The process of gradually optimizing thresholds, such as batch size, learning rate, and detection threshold, involved using the Gray Wolf optimization technique (GWO). The conversion process was accelerated and improved overall detection process efficiency and accuracy using a clever fusion of machine learning and metaheuristic optimization techniques. A key component of our methodology is the careful data processing, which was necessary to ensure. The suggested method was carefully examined on a particular dataset, and the 94% training accuracy that was attained together with an identical test dataset accuracy highlights the method’s resilience. These findings support the effectiveness of our approach in reducing false positives and negatives, resulting in unmatched recall and precision in the detection system. The discovery has significant significance as it can potentially improve face detection systems’ performance and reliability in various real-world applications, such as human-computer interaction and surveillance. Convolutional neural networks, deep learning architectures, and metaheuristic optimization approaches were synergized to produce a new and reliable solution. [ABSTRACT FROM AUTHOR] |
| Abstract (Arabic): | تركز المقالة على تصميم نظام متقدم لاكتشاف الوجوه يدمج شبكة متبقية محسنة (ريزدوال نت-50) ضمن إطار شبكة الالتفاف العصبية السريعة القائمة على المناطق (فاستر آر-سي إن إن)، والتي تم تحسينها باستخدام خوارزمية تحسين الذئب الرمادي (جراي وولف أوبتيمايزيشن). يتناول هذا النهج تحديات مثل تغيرات الإضاءة، والحجب، وتعبيرات الوجه من خلال الجمع بين بنى التعلم العميق والتحسين الميتاهيوريستي لتعزيز استخراج الميزات، ودقة التصنيف، وضبط المعاملات الفائقة. أظهرت النتائج التجريبية على مجموعة بيانات اكتشاف المشاة دقة عالية بلغت 94%، مع مؤشرات قوية للدقة، والاستدعاء، ومقياس التقاطع على الاتحاد (آي أو يو)، إلى جانب أداء فعال في الوقت الحقيقي. تقارن الدراسة بشكل إيجابي مع الطرق الحالية، مبرزة تحسينات في المتانة وجودة الكشف، وتقترح أعمالًا مستقبلية على التكامل متعدد الوسائط وحفظ الخصوصية لتطبيقات أوسع في العالم الحقيقي. [Extracted from the article] |
| Copyright of Iraqi Journal for Electrical & Electronic Engineering is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 194879601 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Designing Face Detection Systems with Gray Wolf Optimization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sabah+Abbod%2C+Noor%22">Sabah Abbod, Noor</searchLink><relatesTo>1</relatesTo><i> noorsabah@uomustansiriyah.edu.iq</i><br /><searchLink fieldCode="AR" term="%22Mohasefi%2C+Jamshid+B%2E%22">Mohasefi, Jamshid B.</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Iraqi+Journal+for+Electrical+%26+Electronic+Engineering%22">Iraqi Journal for Electrical & Electronic Engineering</searchLink>. Jun2026, Vol. 22 Issue 1, p233-244. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Human+facial+recognition+software%22">Human facial recognition software</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: The main objective of this paper project was to create a state-of-the-art face identification technique that can handle the various difficulties caused by changes in illumination, occlusions, and facial emotions. Face detection is a cornerstone of computer vision, facilitating diverse applications ranging from surveillance systems to human-computer interaction. Throughout this paper, the comprehensive exploration of advancing face detection methodologies has been undertaken, culminating in developing and evaluating a novel approach. The challenges posed by variations in facial expressions, lighting conditions, and occlusions necessitated a multifaceted solution. Our proposed method, which consists of interconnected steps, works quite well to overcome these challenges. Using deep learning architectures to increase feature extraction and discrimination was beneficial in the initial stage of fine-tuning Residual Networks (ResNet-50) to serve as the Region-based Convolutional Neural Network (Faster R-CNN) framework classifier. The process of gradually optimizing thresholds, such as batch size, learning rate, and detection threshold, involved using the Gray Wolf optimization technique (GWO). The conversion process was accelerated and improved overall detection process efficiency and accuracy using a clever fusion of machine learning and metaheuristic optimization techniques. A key component of our methodology is the careful data processing, which was necessary to ensure. The suggested method was carefully examined on a particular dataset, and the 94% training accuracy that was attained together with an identical test dataset accuracy highlights the method’s resilience. These findings support the effectiveness of our approach in reducing false positives and negatives, resulting in unmatched recall and precision in the detection system. The discovery has significant significance as it can potentially improve face detection systems’ performance and reliability in various real-world applications, such as human-computer interaction and surveillance. Convolutional neural networks, deep learning architectures, and metaheuristic optimization approaches were synergized to produce a new and reliable solution. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Arabic) Group: Ab Data: تركز المقالة على تصميم نظام متقدم لاكتشاف الوجوه يدمج شبكة متبقية محسنة (ريزدوال نت-50) ضمن إطار شبكة الالتفاف العصبية السريعة القائمة على المناطق (فاستر آر-سي إن إن)، والتي تم تحسينها باستخدام خوارزمية تحسين الذئب الرمادي (جراي وولف أوبتيمايزيشن). يتناول هذا النهج تحديات مثل تغيرات الإضاءة، والحجب، وتعبيرات الوجه من خلال الجمع بين بنى التعلم العميق والتحسين الميتاهيوريستي لتعزيز استخراج الميزات، ودقة التصنيف، وضبط المعاملات الفائقة. أظهرت النتائج التجريبية على مجموعة بيانات اكتشاف المشاة دقة عالية بلغت 94%، مع مؤشرات قوية للدقة، والاستدعاء، ومقياس التقاطع على الاتحاد (آي أو يو)، إلى جانب أداء فعال في الوقت الحقيقي. تقارن الدراسة بشكل إيجابي مع الطرق الحالية، مبرزة تحسينات في المتانة وجودة الكشف، وتقترح أعمالًا مستقبلية على التكامل متعدد الوسائط وحفظ الخصوصية لتطبيقات أوسع في العالم الحقيقي. [Extracted from the article] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Iraqi Journal for Electrical & Electronic Engineering is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194879601 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.37917/ijeee.22.1.22 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 233 Subjects: – SubjectFull: Grey Wolf Optimizer algorithm Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Human facial recognition software Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Computer vision Type: general Titles: – TitleFull: Designing Face Detection Systems with Gray Wolf Optimization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sabah Abbod, Noor – PersonEntity: Name: NameFull: Mohasefi, Jamshid B. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18145892 Numbering: – Type: volume Value: 22 – Type: issue Value: 1 Titles: – TitleFull: Iraqi Journal for Electrical & Electronic Engineering Type: main |
| ResultId | 1 |
