Real-time face alignment: evaluation methods, training strategies and implementation optimization

نویسندگان

چکیده

Abstract Face alignment is a crucial component in most face analysis systems. It focuses on identifying the location of several keypoints human faces images or videos. Although methods and models are available to developers popular computer vision libraries, they still struggle with challenges such as insufficient illumination, extreme head poses, occlusions, especially when constrained by needs real-time applications. Throughout this article, we propose set training strategies implementations based data augmentation, software optimization techniques that help improving large variety belonging algorithms for alignment. We an extended evaluation metrics allow novel evaluations mitigate typical problems found tracking contexts. The experimental results show generated using our proposed faster, smaller, more accurate, robust specific challenging conditions smoother In addition, strategy shows be applicable across different types devices algorithms, making them versatile both academic industrial uses.

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ژورنال

عنوان ژورنال: Journal of Real-time Image Processing

سال: 2021

ISSN: ['1861-8219', '1861-8200']

DOI: https://doi.org/10.1007/s11554-021-01107-w