CAJ | 학술논문

提出了一种基于稀疏分解的不同光照和姿态的人脸识别方法。通过给定的样本为每一类人脸图像训练一个特定的字典，使得在稀疏限制条件下，图像的表示误差最小。将测试图像投影到每一个字典的原子所形成的空间，然后利用误差向量进行分类。为了处理不同光照和姿态问题，采用了基于反照率估计的姿态的重照技术产生同一个人的不同光照条件下的多幅正面图像，从而使得本文方法能够在只有极少数训练图像的条件下获得很高的识别率。通过采用公用数据库中的人脸图像进行验证表明本文方法能够有效的实现不同光照和姿态条件下的人脸识别，其在性能方面比现有大多数方法更优。
제출료일충기우희소분해적불동광조화자태적인검식별방법。통과급정적양본위매일류인검도상훈련일개특정적자전，사득재희소한제조건하，도상적표시오차최소。장측시도상투영도매일개자전적원자소형성적공간，연후이용오차향량진행분류。위료처리불동광조화자태문제，채용료기우반조솔고계적자태적중조기술산생동일개인적불동광조조건하적다폭정면도상，종이사득본문방법능구재지유겁소수훈련도상적조건하획득흔고적식별솔。통과채용공용수거고중적인검도상진행험증표명본문방법능구유효적실현불동광조화자태조건하적인검식별，기재성능방면비현유대다수방법경우。
A face recognition algorithm based on simultaneous sparse approximations under varying illumination and pose is proposed. A dictionary is learned for each class based on given training examples which minimizes the representation error with a sparseness constraint. A novel test image is projected onto the span of the atoms in each learned dictionary and the resulting residual vectors are then used for classification. To handle variations in lighting conditions and pose, an image relighting technique is used to generate multiple frontal images of the same person with variable lighting. As a result, the proposed algorithm has the ability to recognize human faces with high accuracy even when only a single or a very few images per person are provided for training. The efficiency of the proposed method is demonstrated using publicly available databases and it is shown that this method is efficient and can perform significantly better than many competitive face recognition algorithms.