Interpretable Deep Learning for Facial Feature Detection: A Comprehensive Study on Face and Eyes Recognition with LIME Explanations

Facial recognition has been nowadays a key role in computer field. The rapid advancement of Machine Learning techniques has been revolutionizing the field of computer vision. This paper presents a comprehensive investigation into the application of Machine Learning techniques. The study comprehensively reviews the various state-of-the-art facial and ocular algorithms. These modern learning techniques help to concatenate the various model which exist. A detailed examination of dataset and evaluation metrics are used for assessing detection performance is also presented to provide a foundation for comparative analysis. This article is used for designing and implementing of novel models for face and eye detection. Inception v3 was used to assist in removing overfitting using efficient convolution and parallel layers. This paper contributes to the ongoing discourse on Machine learning approaches to face and eye recognition by providing new examples, performance reviews, and ethical considerations. This article focuses on using the capabilities Multi-task Cascaded Convolutional Networks (MTCNN) for precise and efficient face and eye detection. Additionally, the study incorporates interpretability methodologies such as LIME(Local Interpretable Model-agnostic Explanations). The results of this study have important implications for the development of more accurate, more efficient, and more responsible facial and visual recognition systems, widely used in various industries.