Deep Learning-Based Facial Age Estimation Using Convolutional Neural Networks
Facial Age Estimation, Deep Learning, Convolutional Neural Networks, Computer Vision, Age Recognition, Pattern Recognition
Facial age estimation has gained significant attention in computer vision and pattern recognition due to its wide-ranging applications in age-based content filtering, human-computer interaction, and forensic age estimation. This paper presents a novel approach to facial age estimation leveraging Convolutional Neural Networks (CNNs), a deep learning architecture known for its ability to extract hierarchical features from raw pixel data. Our proposed method involves the utilization of CNNs to learn discriminative features from facial images, enabling accurate estimation of age across diverse demographic distributions. The proposed CNN-based age estimation framework consists of multiple convolutional layers followed by fully connected layers, enabling the network to capture intricate patterns associated with facial aging. We employ publicly available datasets for training and evaluation, including the IMDB-WIKI dataset and the Morph dataset, to benchmark the performance of our model. Data preprocessing techniques such as alignment, normalization, and augmentation are incorporated to enhance the quality and diversity of the training data, improving the generalization capability of the model. Evaluation of the proposed method is conducted using standard metrics such as mean absolute error (MAE) and root mean squared error (RMSE), demonstrating its effectiveness in accurately estimating the age of individuals from facial images. Experimental results indicate that our CNN-based approach outperforms traditional methods and achieves state-of-the-art performance in facial age estimation tasks. Furthermore, we analyze the robustness of the proposed model to variations in pose, expression, and illumination, highlighting its ability to generalize across different environmental conditions.
Article Details
Unique Paper ID: 162644

Publication Volume & Issue: Volume 10, Issue 10

Page(s): 622 - 633
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