2019/05/14 by Mingzhen Shao, Shao, Mingzhen, Zhun Sun +6 · 2 citations
Computer Science · Engineering · #Artificial intelligence #Benchmark (surveying) #Bounding overwatch #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Engineering #Estimation #FOS: Computer and information sciences #Face (sociological concept) #Face recognition and analysis #Gait Recognition and Analysis #Geography #Geology #Head (geology) #Human Pose and Action Recognition #Image (mathematics) #Machine learning #Margin (machine learning) #Minimum bounding box #Pose #Simplicity #cs.CV
paper · pdf · doi:10.48550/arxiv.1905.08609
published in arXiv (Cornell University) (Cornell University) · IEEE International Conference on Automatic Face & Gesture Recognition (FG2019)
arxiv created 2019/05/14 · openalex publication_date 2019/05/14 · arxiv updated 2019/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We address a problem of estimating pose of a person's head from its RGB image. The employment of CNNs for the problem has contributed to significant improvement in accuracy in recent works. However, we show that the following two methods, despite their simplicity, can attain further improvement: (i) proper adjustment of the margin of bounding box of a detected face, and (ii) choice of loss functions. We show that the integration of these two methods achieve the new state-of-the-art on standard benchmark datasets for in-the-wild head pose estimation.