CAJ | 학술논문

针对传统的三维人脸识别算法受光照、姿态、表情及场景变化影响导致耗时过多及成本过高的问题，提出了一种基于均值漂移线性判别分析优化尺度不变特征融合（FSIF）算法。使用均值漂移线性判别分析找到五个类似于查询人脸的最佳候选类；利用尺度不变特征融合提取出候选人脸及查询人脸的融合特征描述符，并进行特征匹配得到目标人脸；根据特征描述符的匹配关键点数目完成人脸的识别。在USCD/Honda、FRGC v2及自己搜集的人脸数据集上的实验结果表明，该算法解决了降低FSIF人脸识别的计算复杂度，并在不降低识别性能的前提下大大地节约了成本，相比几种较为先进的三维人脸识别算法，该算法取得了更好的识别效果。
침대전통적삼유인검식별산법수광조、자태、표정급장경변화영향도치모시과다급성본과고적문제，제출료일충기우균치표이선성판별분석우화척도불변특정융합（FSIF）산법。사용균치표이선성판별분석조도오개유사우사순인검적최가후선류；이용척도불변특정융합제취출후선인검급사순인검적융합특정묘술부，병진행특정필배득도목표인검；근거특정묘술부적필배관건점수목완성인검적식별。재USCD/Honda、FRGC v2급자기수집적인검수거집상적실험결과표명，해산법해결료강저FSIF인검식별적계산복잡도，병재불강저식별성능적전제하대대지절약료성본，상비궤충교위선진적삼유인검식별산법，해산법취득료경호적식별효과。
Traditional three-dimensional face recognition algorithms take too long time and high costs due to variations of illustration, poses, expression and scene, so a fusion of scale invariant features algorithm optimized by shifting mean linear discriminant analysis is proposed. Shifting mean linear discriminant analysis is used to find five optimal candidate classes similar to query faces. Fusion of scale invariant features is applied in extracting fusion feature descriptors of candidate faces and querying face, after which feature matching is done so as to get objective face. Matching point number of the feature descriptors is referred to finish face recognition. Experimental results on USCD/Honda、FRGC v2 and face dataset collected by self show that proposed algorithm decreases the computational time of FSIF-based face recognition and saves costs clearly without decreasing the recognition performance. It has better recognition efficiency than several advanced three-dimensional face recognition algorithms.