Papers › Deep learning and machine learning techniques for head pose estimation: a survey
Deep learning and machine learning techniques for head pose estimation: a survey
Redhwan Algabri, Ahmed Abdu, Sungon Lee
Head pose estimation (HPE) has been extensively investigated over the past decade due to its wide range of applications across several domains of artificial intelligence (AI), resulting in progressive improvements in accuracy. The problem becomes more challenging when the application requires full-range angles, particularly in unconstrained environments, making HPE an active research topic. This paper presents a comprehensive survey of recent AI-based HPE tasks in digital images. We also propose a novel taxonomy based on the main steps to implement each method, broadly dividing these steps into eleven categories under four groups. Moreover, we provide the pros and cons of ten categories of the overall system. Finally, this survey sheds some light on the public datasets, available codes, and future research directions, aiding readers and aspiring researchers in identifying robust methods that exhibit a strong baseline within the subcategory for further exploration in this fascinating area. The review compared and analyzed 113 articles published between 2018 and 2024, distributing 70.5% deep learning, 24.1% machine learning, and 5.4% hybrid approaches. Furthermore, it included 101 articles related to datasets, definitions, and other elements for AI-based HPE systems published over the last two decades. To the best of our knowledge, this is the first paper that aims to survey HPE strategies based on artificial intelligence, with detailed explanations of the main steps to implement each method. A regularly updated project page is provided: (https://github.com/Redhwan-A/SurveyPHE/blob/main/README.md).
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