CNN — Forest Based Person Identification and Head Pose Estimation for AI Based Applications

Autor:	D. Anitta, A. Annis Fathima
Rok vydání:	2023
Předmět:	Artificial Intelligence Control and Systems Engineering Software Information Systems
Zdroj:	International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 31:47-63
ISSN:	1793-6411 0218-4885
DOI:	10.1142/s0218488523400044
Popis:	Face recognition and head posture estimation have aroused a lot of academic interest recently since the inherent information improves the performance of face-related applications such as face alignment, augmented reality, healthcare applications, and emotion detection. The proposed work explores the challenges of identifying people and determining head posture. An analysis of the features produced at intermediate layers by limiting the number of kernals is performed and improved the performance of detecting the person. In addition, the learned features are sent to forest trees in order to determine the exact head attitude of the detected person. The proposed Forest CNN (FCNN) architecture is tested for head pose estimation methods on the Pointing 04 and Facepix benchmark databases.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4f59a5690c7e71c31cf769d72d976e0a https://doi.org/10.1142/s0218488523400044 Zobrazit plný text záznamu