Image classification is a fundamental task in computer vision, with numerous applications across various industries. Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have shown remarkable performance in this domain. However, identifying crucial features can differ between tasks, potentially impacting model efficiency. To address this challenge, our work focuses on extracting the most informative features before training. We term these features as "informative" because they possess the greatest capacity to influence the classification outcome. By leveraging these features during both training and validation, we can enhance the overall performance of the model.
Informative Feature Extraction
Our approach involves utilizing 3D Morphable Model (3DMM) coefficients to extract informative features from facial images. These coefficients capture the underlying anatomy of the face, enabling the identification of subtle details that are crucial for accurate classification. By exploiting these coefficients, we can reduce the complexity in the feature space while preserving the essential characteristics of each input. This simplification leads to a more efficient model development process, as it reduces the number of unnecessary features that need to be processed during training.
Classification Task Comparison
We compare the performance of our proposed method with the original images in three classification tasks: Face Recognition, Classify Expression, and Classify Gender. The results demonstrate a significant reduction in training time when using 3DMM coefficients instead of the original images. On average, the training time is reduced by factors ranging from approximately 2 to 62 times, depending on the task. This improvement enables faster model development, deployment, and real-time classification applications.
Conclusion
Our work presents a novel approach for enhancing image classification by extracting informative features before training. By leveraging these features during both training and validation, we can boost the performance of the model without compromising its accuracy. The proposed method demonstrates a significant reduction in training time, making it more suitable for real-time applications. As the field of computer vision continues to evolve, our approach has the potential to significantly impact various industries that rely on image classification.
