Hybrid Deep Learning and Swarm Intelligence Framework for Accurate Age Estimation from Wrinkles
DOI:
https://doi.org/10.52783/jns.v14.4166Keywords:
Facial wrinkles, accurate age, CNN, HOG, PSO, swarm intelligenceAbstract
Facial wrinkles, influenced by skin type and muscle contraction, are vital indicators of aging. Accurate age estimation from these patterns has significant applications in healthcare, security, and marketing. This paper introduces a robust framework integrating Deep Learning with Hybrid Swarm Intelligence (DL-HSI) for precise age prediction. Preprocessing ensures consistency in input data, followed by feature extraction using Histogram of Oriented Gradients (HOG). These features are fused and optimized through a hybrid approach leveraging Particle Swarm Optimization (PSO), enhancing feature selection and representation. Age group classification is performed using deep Convolutional Neural Networks (CNNs), which further refine predictions to estimate specific ages within groups. Experimental evaluation on the FG-NET dataset demonstrates the proposed method's superior performance, achieving higher accuracy and lower mean absolute error compared to traditional approaches. This unified framework underscores its potential for automated, reliable age estimation from facial wrinkles, offering valuable insights for diverse real-world applications. The proposed method achieves the ratio of accuracy by 98.08%, performance by 97.75%, MAE by 34.56%, processing time by 97.12% and future extraction efficiency by 96.34%.
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