| 孙倩,毕鹏飞.鲁棒判别二维主成分分析在人脸图像识别中的应用[J].电子测量与仪器学报,2026,40(6):64-77 |
| 鲁棒判别二维主成分分析在人脸图像识别中的应用 |
| Robust discriminant two-dimensional principal componentanalysis for face recognition |
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| DOI： |
| 中文关键词: 二维主成分分析 对比学习 自适应正弦角度 特征提取 人脸识别 |
| 英文关键词:two-dimensional principal component analysis (2DPCA) contrastive learning adaptive sinusoidal angle feature extraction face recognition |
| 基金项目:江苏省基础研究计划基金(BK20220452)项目资助 |
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| 摘要点击次数: 75 |
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| 中文摘要: |
| 目前,二维主成分分析方法(two-dimensional principal component analysis, 2DPCA)因其固有的小协方差矩阵特性已在人脸识别中获得广泛应用。然而,在现实世界中,采集到的人脸图像往往会受到噪声干扰,导致现有许多基于2DPCA的方法存在特征提取不稳定、无法有效感知到类别间关键判别信息的问题。为了克服上述局限,提出一种新颖的鲁棒判别二维主成分分析方法(RD-2DPCA)并在人脸识别领域中开展应用。首先,引入原型驱动的动量对比学习机制,其通过动量更新与在线聚类构建语义一致的正负样本对以收缩类内散度,从而形成可分性更强的判别子空间投影;其次,在该投影上进一步嵌入自适应正弦角度约束,其在保证其解与保护数据全局结构的加权协方差矩阵存在紧密关联的同时,进一步缓解了离群点对最终投影方向的影响;最后,针对优化求解问题,采用黎曼梯度更新与自设计的非贪婪迭代算法的组合策略,使其能稳定收敛至判别引导下的鲁棒最优解。为了验证方法的可行性,选取3个公开人脸数据集进行实验,各个数据集上取得的最优识别精度分别为73.90%、61.31%、87.74%。综合各项实验结果表明,RD-2DPCA在判别性、鲁棒性与收敛稳定性等方面相较单阶段或纯2DPCA变体展现出更为优越的性能表现,充分验证了该方法在复杂观测环境下的抗干扰能力与实际应用价值。 |
| 英文摘要: |
| Currently, the two-dimensional principal component analysis (2DPCA) method has been widely applied in facial recognition due to its inherent low-covariance matrix characteristic. However, in the real world, captured facial images are often subject to noise interference, leading to issues such as unstable feature extraction and the inability to effectively perceive key discriminative information between categories in many existing 2DPCA-based methods. To overcome these limitations, a novel robust discriminative 2DPCA (RD-2DPCA) method is proposed and applied to facial recognition. First, this method introduces a prototype-driven momentum contrastive learning mechanism. Through momentum updates and online clustering, it constructs semantically consistent positive-negative sample pairs to reduce intra-class divergence, thereby forming a more separable discriminative subspace projection. Second, an adaptive sine angle constraint is embedded in the projection. This constraint ensures a strong association between the solution and the weighted covariance matrix that preserves the global data structure, while further mitigating the influence of outliers on the final projection direction. Finally, to address the optimization problem, a combined strategy of Riemannian gradient updates and a custom non-greedy iterative algorithm ensures stable convergence to a robust optimal solution guided by discriminative principles. To validate the feasibility of this method, experiments were conducted on three publicly available facial datasets, achieving optimal recognition accuracies of 73.90%, 61.31%, and 87.74% respectively. Comprehensive experimental results demonstrate that RD-2DPCA exhibits superior performance compared to single-stage or pure 2DPCA variants in terms of discriminative capability, robustness, and convergence stability. This fully validates the method’s interference resistance and practical application value in complex observation environments. |
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