A Research on Fast Face Feature Points Detection on Smart Mobile Devices

We explore how to leverage the performance of face feature points detection on mobile terminals from3 aspects. First, we optimize the models used in SDM algorithms via PCA and Spectrum Clustering. Second, we propose an evaluation criterion using Linear Discriminative Analysis to choose the best local feature descriptions which plays a critical role in feature points detection. Third, we take advantage ofmulticore architecture of mobile terminal and parallelize the optimized SDMalgorithm to improve the efficiency further. The experiment observations show that our final accomplished GPC-SDM (improved Supervised Descent Method using spectrum clustering, PCA, and GPU acceleration) suppresses the memory usage, which is beneficial and efficient to meet the realtime requirements.