Head pose estimation (HPE) is a crucial task in pose recognition, but the existing HPE methods suffer from low robustness, low accuracy and inconvenience of contact measurement. In this paper, we build up an infrared structured light system for head pose estimation and propose an adaptive head pose estimation method to improve robustness and accuracy without the need for training. Firstly, we utilize a dynamic structured light system to acquire both the standard model and real-time dynamic data. Then, an iterative registration algorithm is proposed to adaptively segment the facial region which remains stable excluding distractions such as expressions and estimate the head pose. We experimentally evaluate the effectiveness of our method under conventional and large-angular head motion, and different expressions. The results demonstrate our method achieves highly accurate and robust real-time head pose estimation in a contactless manner.

SUMMARY: Variant Call Format (VCF), the prevailing representation for germline genotypes in population sequencing, suffers rapid size growth as larger cohorts are sequenced and more rare variants are discovered. We present Sparse Project VCF (spVCF), an evolution of VCF with judicious entropy reduction and run-length encoding, delivering >10× size reduction for modern studies with practically minimal information loss. spVCF interoperates with VCF efficiently, including tabix-based random access. We demonstrate its effectiveness with the DiscovEHR and UK Biobank whole-exome sequencing cohorts. AVAILABILITY AND IMPLEMENTATION: Apache-licensed reference implementation: github.com/mlin/spVCF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.