CAJ | 학술논문

非限制环境下光照、姿势、表情等变化已成为户外人脸识别的主要瓶颈所在。针对这一问题，提出了一种学习原型超平面融合线性判别边信息的算法进行人脸识别。利用支持向量机将弱标记数据集中的每个样本表示成一个原型超平面中层特征；使用学习组合系数从未标记的通用数据集中选择支持向量稀疏集；借助于Fisher线性判别准则最大化未标记数据集的判别能力，并使用迭代优化算法求解目标函数；利用线性判别边信息进行特征提取、余弦相似性度量以完成最终的人脸识别。在Extended YaleB和户外标记人脸（LFW）和通用人脸数据集上进行实验，验证了所提算法的有效性和可靠性。实验结果表明，相比其他几种较为先进的人脸识别算法，所提算法取得更好的识别性能。
비한제배경하광조、자세、표정등변화이성위호외인검식별적주요병경소재。침대저일문제，제출료일충학습원형초평면융합선성판별변신식적산법진행인검식별。이용지지향량궤장약표기수거집중적매개양본표시성일개원형초평면중층특정；사용학습조합계수종미표기적통용수거집중선택지지향량희소집；차조우Fisher선성판별준칙최대화미표기수거집적판별능력，병사용질대우화산법구해목표함수；이용선성판별변신식진행특정제취、여현상사성도량이완성최종적인검식별。재Extended YaleB화호외표기인검（LFW）화통용인검수거집상진행실험，험증료소제산법적유효성화가고성。실험결과표명，상비기타궤충교위선진적인검식별산법，소제산법취득경호적식별성능。
In unconstrained condition, illumination, posture and facial expression becomes the main choke point of wild face recognition. Based on this issue, a fusion algorithm is proposed with learning prototype hyperplanes and side-Information based linear discriminant analysis is proposed. Support Vector Machine(SVM)model is used to each sample in weak labeled data set to be mid-level feature of prototype hyperplanes, and SVM sparse set is selected from generic data set without labeled by learning combination coefficient. Fisher discriminative criterion is used to maximize discriminat ability under the constraint of combination sparse coefficient of SVM model, and the objective function is solved by iterative optimization algorithm. SILD is used to extract features and cosine similarity measure is used to finish face recognition. The effectiveness and reliability of proposed algorithm has been verified by experiments on the two common databases Extended YaleB and labeled face of wild(LFW). The results show that proposed algorithm has better recognition efficiency comparing with several other face recognition algorithms.