An Improved Face Detection Method Based on Face Recognition Application

Qinfeng Li
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引用次数: 5

Abstract

Face recognition technology has been widely studied and applied for decades, and deep neural networks have greatly improved the indicators of face recognition systems. However, the face recognition application in reality is still subject to interference caused by direction, occlusion, shading and dynamic background, which makes the face recognition system unstable. This paper proposes an improved diagonal detection method, using a K parallel bottleneck connection structure, spacing parameters in each bottleneck connection structure, and using parameter partition sharing to reduce overfitting. The new loss function refines the difference between the detected corner point and the ground truth under different conditions, which can further improve the detection accuracy. Both the standard data set and the experiments in the real environment show that the proposed method has better detection accuracy and robustness.
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一种基于人脸识别应用的改进人脸检测方法
人脸识别技术几十年来得到了广泛的研究和应用,深度神经网络极大地改善了人脸识别系统的各项指标。然而,现实中的人脸识别应用仍然会受到方向、遮挡、阴影和动态背景等因素的干扰,使得人脸识别系统不稳定。本文提出了一种改进的对角检测方法,采用K个平行瓶颈连接结构,每个瓶颈连接结构中间隔参数,并采用参数划分共享的方法减少过拟合。新的损失函数细化了不同条件下检测到的角点与地面真值的差值,进一步提高了检测精度。标准数据集和实际环境下的实验结果表明,该方法具有较好的检测精度和鲁棒性。
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