POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search

• URL: http://arxiv.org/abs/2410.09583v2
• Date: Tue, 15 Oct 2024 15:31:06 GMT
• Title: POPoS: Improving Efficient and Robust Facial Landmark Detection with Parallel Optimal Position Search
• Authors: Chong-Yang Xiang, Jun-Yan He, Zhi-Qi Cheng, Xiao Wu, Xian-Sheng Hua,
• Abstract summary: This paper introduces the Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework. POPoS employs three key innovations: Pseudo-range multilateration is utilized to correct heatmap errors, enhancing the precision of landmark localization. A single-step parallel algorithm is introduced, significantly enhancing computational efficiency and reducing processing time.
• Score: 34.50794776762681
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving a balance between accuracy and efficiency is a critical challenge in facial landmark detection (FLD). This paper introduces the Parallel Optimal Position Search (POPoS), a high-precision encoding-decoding framework designed to address the fundamental limitations of traditional FLD methods. POPoS employs three key innovations: (1) Pseudo-range multilateration is utilized to correct heatmap errors, enhancing the precision of landmark localization. By integrating multiple anchor points, this approach minimizes the impact of individual heatmap inaccuracies, leading to robust overall positioning. (2) To improve the pseudo-range accuracy of selected anchor points, a new loss function, named multilateration anchor loss, is proposed. This loss function effectively enhances the accuracy of the distance map, mitigates the risk of local optima, and ensures optimal solutions. (3) A single-step parallel computation algorithm is introduced, significantly enhancing computational efficiency and reducing processing time. Comprehensive evaluations across five benchmark datasets demonstrate that POPoS consistently outperforms existing methods, particularly excelling in low-resolution scenarios with minimal computational overhead. These features establish POPoS as a highly efficient and accurate tool for FLD, with broad applicability in real-world scenarios. The code is available at https://github.com/teslatasy/PoPoS

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