Facial Landmark Detection Using Retraining R. Vince Rabsatt

1. Facial Landmark Detection Using Retraining R. Vince Rabsatt Prof. IoannisKakadiaris, Dr. Boris Efros, ChengweiHuang Computational Biomedicine Lab, Departments of Computer Science University of Houston, CBL has developed a point-landmark detector that applies deformations to sub-shapes that transform each point-landmark into a target landmark location. However, this model was restricted by its training data. The previous model required that all training data must be available, and was unable to incorporate new data. This model has been modified to include a retraining operation . The retraining operation is a combination of training and testing that adapt the existing model to newly observed data. • Essential part of computer vision, such as expression recognition and face alignment • Makes model more dynamic by its ability to retrain new data • Can be used for tracking and site adaptation Landmark Detection Framework • Explore the sensitivity of a retraining algorithm with different parameters. • Determine a learning rate sequence that produce a high probability for landmark detection • Determine an adequate sample size for retraining that generates a desirable detection of landmarks Retraining Process Retraining Data Training Data Learning Parameters Predict New Data Retraining Operation Face Detector Model Steps of Face Detector Facial landmark detection is an vital component of computer vision. More flexibility is added to CBL’s face detection model by adding a retraining operation. With retraining the model can be updated for tracking, site adaptations, or other variations in data. The ability to retrain small samples of new data can also cut down on the time required to train a large set of data. Different learning rate sequences effect the probability of detection in different ways. A High Slow Learning Rate Sequences seems to produces the best results. When updating a model with new data one image increases the probability of detection significantly. However, each image after that improves the probability of detection by a very minuscule amount. Update Retrain model so it will be able to detect landmarks on subjects with glasses. Iterative process of detecting facial landmarks Experiment 1 Experiment 2 Adjusting the Learning Rate Sequence Varying Images in Retraining • The sensitivity of the retraining algorithm is controlled by what we refer to as a “learning rate” sequence • Adjust the parameters of the learning rate sequence to discover one that produces the best results • Utilize the optimal learning rate sequence from experiment one • Use a set number of subjects for testing but vary amount in retraining • One image increases detection by 25% B. Efraty, C. Huang, S.K. Shah and I.A. Kakadiaris. Facial Landmark Detection in Uncontrolled Conditions. UH Computational Biomedicine Lab, 2011.