Advancements in machine learning (ML) have significantly enhanced image processing algorithms, leading to notable improvements in face recognition technology. The paper introduces a framework, FaceWave, which uses a support vector machine (SVM) model combined with wavelet transformation to extract vital facial features from female cricketers. The integration addresses the existing challenges of identifying facial features in context of lighting, posture, and angles. In the framework, raw images are first collected using the Google Chrome Fatkun addon. These images undergo a thorough preprocessing stage to enhance their quality and remove any noise or artifacts. Subsequently, wavelet transformation is applied to derive significant features from the facial images. The processed feature vectors are then utilized to train our SVM model. Our model demonstrates impressive accuracy, achieving 76% during training and 84% during testing. These results highlight the robustness of FaceWave in accurately identifying female cricketers, showcasing the potential of our customized approach in this specialized application.