复杂环境下的陌生人脸匹配，即在人脸存在光照、姿态干扰时，判断两张在训练集中从未出现过的人脸照片是否代表同一个人。在预处理阶段，采用多尺度视皮层算法，降低光照的影响，提出并采用基于PCA-SIFT特征的图片融合算法无监督地对齐人脸，降低人脸姿态的影响。在识别阶段，提出并采用半随机池化方法优化了局部卷积限制波尔兹曼机网络的稳定性，习得深度特征后采用基于信息熵的度量学习算法计算马氏距离并通过SVM分类识别。实验结果显示，提出的方法在LFW数据集上取得了78%的识别率，相比于采用相同训练模式的经典度量学习方法取得了7%的提高，验证了所提方法的有效性。%The goal of unseen face matching under complex environment is to decide whether two pictures outside the training set belong to the same person, under pose and illumination factors in the image. Leveraging the intra-person subspace similarity metric learning algorithm (sub-SML) as a framework, a multi-scale retinex algorithm is applied to reduce illumination in the images; a PCA-SIFT enhanced unsupervised alignment algorithm, congealing, is proposed and applied to align face images and makes sure all faces in images are at frontal and central position;a semi-stochastic pooling method is proposed and applied to local convolutional RBM to enhance the stability of unsupervised feature learning. And finally the learned features are applied to information theoretic metric learning to calculate the Mahalanobis distance and execute the proposed unseen face matching task via SVM classification. We used the Labeled-Faced-in-the-Wild (LFW) data set to run our algorithm, following the restricted-image training/testing model by the LFW author. Experimental results on LFW show 78%accuracy of proposed method, 7%higher than classic metric learning me-thod under the same training/testing model, validating the effectiveness of proposed method.
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