Designing Face Detection Systems with Gray Wolf Optimization.
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| Title: | Designing Face Detection Systems with Gray Wolf Optimization. |
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| 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] |
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| Database: | Engineering Source |
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