Facial landmark detection using artificial intelligence techniques

In facial landmark detection processing, facial images can be divided into subsets containing faces in similar positions and facial properties in an uncontrolled way. Existing facial detection methods for deep learning have been achieved outstanding performance. Those inspired by biological computing models can far overcome past forms of artificial intelligence in common learning tasks. However, these methods do not specifically incorporate Structural frameworks between points of reference. This study addresses facial identity detection problems and offers a different analysis of Deep Learning Neural Network midterm layers (DNN). The proposed Tweaked Adaptive Architecture of the DNN (TA2DNN) specialized in regressing face marker coordinates in specific poses and appearances. This method presents data improvement techniques for addressing the lack of training data in excessive-profile cases to provide adequate training for each of those highly skilled subnetworks and existing landmark detection methods. This is mainly done using advanced algorithms for facial detection and traditional methods for edge and corner spot detection using Shi filters. Finally, developing code and trained models publicly promote the findings' replicability and demonstrate superior performance to existing state-of-the-art facial landmark detection techniques and greater generalization of high-profile and occlusive data. Better accuracy of detection provenMean Error Analysis Ratio (MEAR) as 0.38% and Comparative Analysis of Face Detection 98.5% and further elaborated with more data-training models and depictions.